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  • Opportunities for AI in Accessibility

    Opportunities for AI in Accessibility

    I thoroughly enjoyed reading Joe Dolson’s most recent article on the crossroads of AI and availability because of how skeptical he is of AI in general and how many people have been using it. Despite working for Microsoft as an affordability technology strategist and managing the AI for Accessibility grant program, I’m pretty skeptical of AI. As with any tool, AI can be used in quite productive, equitable, and visible ways, and it can also be used in dangerous, unique, and dangerous ones. And there are a lot of uses for the poor center as well.

    I’d like you to consider this a “yes … and” piece to complement Joe’s post. Instead of refuting everything he’s saying, I’m pointing out some areas where AI may make real, positive impacts on people with disabilities. To be clear, I’m not saying that there aren’t true threats or pressing problems with AI that need to be addressed—there are, and we’ve needed to address them, like, yesterday—but I want to take a little time to talk about what’s possible in hope that we’ll get there one day.

    Other words

    Joe’s article spends a lot of time examining how computer vision versions can create other words. He raises a number of true points about the state of affairs right now. And while computer-vision concepts continue to improve in the quality and complexity of information in their information, their benefits aren’t wonderful. He argues to be accurate that the state of image research is currently very poor, especially for some graphic types, in large part due to the absence of contextual contexts in which to look at images ( as a result of having separate “foundation” models for words analysis and image analysis ). Today’s models aren’t trained to distinguish between images that are contextually relevant ( that should probably have descriptions ) and those that are purely decorative ( which might not need a description ) either. However, I still think there’s possible in this area.

    As Joe mentions, human-in-the-loop publishing of alt word should definitely be a factor. And if AI can intervene and provide a starting point for alt text, even if the rapid reads,” What is this BS?” That’s not correct at all … Let me try to offer a starting point— I think that’s a win.

    If we can specifically station a design to examine image usage in context, it might help us more quickly determine which images are likely to be elegant and which ones are likely to need a description. That will clarify which situations require image descriptions, and it will increase authors ‘ effectiveness in making their sites more visible.

    The image example provided in the GPT4 announcement provides an intriguing opportunity, even though complex images like graphs and charts are challenging to summarize succinctly ( even for humans ). Let’s say you came across a map that merely stated the chart’s name and the type of representation it was:” Pie chart comparing smartphone use to have phone usage in US households making under$ 30, 000 annually.” ( That would be a pretty bad alt text for a chart because it would frequently leave many unanswered questions about the data, but let’s just assume that that was the description in place. ) Imagine a world where people could ask questions about the vivid if their browser knew that it was a pie chart ( because an onboard model determined this ).

    • Perform more people use have telephones or smartphones?
    • How many more?
    • Exists a group of people who don’t fall under either of these categories?
    • How many is that?

    Setting aside the realities of large language model ( LLM) hallucinations—where a model just makes up plausible-sounding “facts” —for a moment, the opportunity to learn more about images and data in this way could be revolutionary for blind and low-vision folks as well as for people with various forms of color blindness, cognitive disabilities, and so on. It might also be helpful in education settings to assist those who can see these graphs as they are able to comprehend the information in the charts.

    What if you could request your website to make a complicated chart simpler? What if you asked it to separate a single line from a range curve? What if you could request your website to change the color combinations in your website so that it works better for your type of color blindness? What if you asked it to switch shades in favor of habits? Given these resources ‘ chat-based interface and our existing ability to manipulate photos in today’s AI devices, that seems like a chance.

    Now imagine a specially designed model that could take the data from that chart and convert it to another format. For example, perhaps it could turn that pie chart ( or better yet, a series of pie charts ) into more accessible ( and useful ) formats, like spreadsheets. That would be amazing!

    Matching algorithms

    When Safiya Umoja Noble chose to put her book Algorithms of Oppression, she hit the nail on the head. Although her book focused on the ways that search engines can foster racism, I believe it’s equally true that all computer models have the potential to foster conflict, prejudice, and intolerance. We all know that poorly designed and maintained algorithms are incredibly harmful, whether it’s Twitter that keeps bringing you the most recent tweet from a drowsy billionaire, YouTube that keeps us in a q-hole, or Instagram that keeps us guessing what natural bodies look like. Many of these are the result of a lack of diversity in the people who create and build them. When these platforms are built with inclusively baked in, however, there’s real potential for algorithm development to help people with disabilities.

    Take Mentra, for example. They serve as a network of employment for people who are neurodivers. Based on more than 75 data points, they match job seekers with potential employers using an algorithm. On the job-seeker side of things, it considers each candidate’s strengths, their necessary and preferred workplace accommodations, environmental sensitivities, and so on. On the employer side, it considers each work environment, communication factors related to each job, and the like. Mentra made the decision to change the script when it came to the typical employment websites because it was run by neurodivergent people. They lower the emotional and physical labor on the job-seeker side of things by recommending available candidates to companies who can then connect with job seekers they are interested in.

    More people with disabilities can be used to create algorithms, which can lessen the likelihood that they will harm their communities. That’s why diverse teams are so important.

    Imagine if the social media company’s recommendation engine was tuned to prioritize follow recommendations from people who discussed topics of interest to those who were fundamentally different from your current sphere of influence. For instance, if you follow a group of white men who are not white or aren’t white and who also discuss AI, it might be wise to follow those who are also disabled or who are not white. If you took its recommendations, perhaps you’d get a more holistic and nuanced understanding of what’s happening in the AI field. These same systems should also use their understanding of biases about particular communities—including, for instance, the disability community—to make sure that they aren’t recommending any of their users follow accounts that perpetuate biases against (or, worse, spewing hate toward ) those groups.

    Other ways that AI can helps people with disabilities

    I’m sure I could go on and on about using AI to assist people with disabilities, but I’m going to make this last section into a bit of a lightning round if I weren’t trying to put this together in between other tasks. In no particular order:

      Voice preservation. You may have seen the VALL-E paper or Apple’s Global Accessibility Awareness Day announcement or you may be familiar with the voice-preservation offerings from Microsoft, Acapela, or others. People who have ALS ( Lou Gehrig’s disease ), motor-neuron disease, or other medical conditions that can prevent them from talking can greatly benefit from having an AI model that can mimic your voice. This is, of course, the same tech that can also be used to create audio deepfakes, so it’s something that we need to approach responsibly, but the tech has truly transformative potential.
    • Voice recognition. Researchers are assisting people with disabilities in the collection of recordings of people with atypical speech, thanks to the assistance of the Speech Accessibility Project. As I type, they are actively recruiting people with Parkinson’s and related conditions, and they have plans to expand this to other conditions as the project progresses. More people with disabilities will be able to use voice assistants, dictation software, and voice-response services, as well as to use only their voices to control computers and other devices, according to this research.
    • Text transformation. The most recent generation of LLMs is capable of altering already-existing text without giving off hallucinations. This is incredibly empowering for those who have cognitive disabilities and who may benefit from text summaries, simplified versions, or even text that has been prepared for Bionic Reading.

    The value of various teams and sources of data

    We must acknowledge the importance of our differences. The intersections of the identities that we exist in have an impact on our lived experiences. These lived experiences—with all their complexities ( and joys and pain ) —are valuable inputs to the software, services, and societies that we shape. Our differences must be reflected in the data we use to develop new models, and those who provide that valuable information must be compensated for doing so. More robust models are produced by inclusive data sets, which promote more justifiable outcomes.

    Want a model that doesn’t demean or patronize or objectify people with disabilities? Make sure that you include information about disabilities that is written by people who have a range of disabilities and that is well represented in the training data.

    Want a model that doesn’t use ableist language? You might be able to use already-existing data sets to create a filter that can read ableist language before it is read. That being said, when it comes to sensitivity reading, AI models won’t be replacing human copy editors anytime soon.

    Want a coding copilot who can provide you with useful recommendations after the jump? Train it on code that you know to be accessible.


    I have no doubt that AI can and will harm people … today, tomorrow, and well into the future. But I also believe that we can acknowledge that and, with an eye towards accessibility ( and, more broadly, inclusion ), make thoughtful, considerate, and intentional changes in our approaches to AI that will reduce harm over time as well. Today, tomorrow, and well into the future.


    Many thanks to Kartik Sawhney for helping me with the development of this piece, Ashley Bischoff for her invaluable editorial assistance, and, of course, Joe Dolson for the prompt.

  • Beware the Cut ‘n’ Paste Persona

    Beware the Cut ‘n’ Paste Persona

    This Person Does Not Exist is a website that uses a machine learning algorithm to create individual faces. It takes actual photos and recombines them into false people faces. We just squinted past a LinkedIn article that claimed this site might be helpful “if you are developing a image and looking for a photo.”

    We concur that computer-generated heads may be excellent candidates for personas, but not for the purpose you might think otherwise. Ironically, the website highlights the core issue of this very common design method: the person ( a ) does not exist. Personas are deliberately created, just like in the photos. Knowledge is combined into a sporadic, unreliable preview that is taken out of context.

    But strangely enough, manufacturers use personalities to encourage their style for the real world.

    Personas: A action up

    Most manufacturers have at least once in their careers created, used, or encountered personalities. In their content” Personas- A Plain Introduction”, the Interaction Design Foundation defines profile as “fictional characters, which you create based upon your study in order to reflect the unique user types that might use your service, product, site, or brand”. Personas typically include a title, profile picture, rates, populations, goals, wants, behavior in relation to a particular service or product, feelings, and desires ( for instance, see Creative Companion’s Persona Core Poster ). According to design firm Designit, the goal of personas is to “make the research relateable, ]and ] easy to communicate, digest, reference, and apply to product and service development.”

    The decontextualization of identities

    People are well-known because they make “dry” study information relevant and more people. However, this approach places a cap on the author’s ability to exclude the target customers from their particular contexts. As a result, personalities don’t describe important factors that make you know their decision-making method or allow you to connect to users ‘ thoughts and behavior, they lack stories. You are aware of the persona’s actions, but you lack the knowledge to know why. You end up with less human-like user images.

    This “decontextualization” we see in identities happens in four way, which we’ll discuss below.

    People are assumed to be stable, according to individuals.

    Here’s a painfully obvious truth: people are not a fixed set of features. Although many businesses still try to box in their employees and customers with outdated personality tests ( referring to you, Myers-Briggs ), You act, think, and feel different according to the situations you experience. You may work pleasant to some people, or you might act rude to others because you appear distinct to different people. And you constantly refute the selections you’ve made.

    Modern psychology agree that while persons usually behave according to certain styles, it’s actually a combination of history and culture that determines how people act and take decisions. The context determines the kind of person you are at each particular time, including the environment, the effect of other people, your mood, and the whole story that led up to a situation.

    Personas present a user as a fixed set of features in an effort to simplify reality, but do so without taking this variability into account. Like personality tests, personas snatch people away from real life. Even worse, people are labeled as” that kind of person” with no means to exercise their innate flexibility and are reduced to a label. This behavior defies stereotypes, diminishes diversity, and doesn’t reflect reality.

    Personas focus on individuals, not the environment

    You’re designing for a context, not an individual, in the real world. There are environmental, political, and social factors to consider when a person lives in a family, a community, or an ecosystem. A design is never meant for a single user. Instead, you create a product that is intended to be used by a certain number of people. However, personal experiences don’t explicitly describe how a user feels about the environment. Instead, they show the user only.

    Would you always make the same decision over and over again? Despite your pledge to eat vegan, you may still choose to purchase some meat when your relatives visit. Your decisions, including your behavior, opinions, and statements, are not only completely accurate but highly contextual because they vary with various circumstances and variables. The persona that “represents” you wouldn’t take into account this dependency, because it doesn’t specify the premises of your decisions. It doesn’t offer a justification for why you act in the way you do. People practice the well-known attribution error, which states that they too often attribute others ‘ behavior to their personalities and not to the circumstances.

    As mentioned by the Interaction Design Foundation, personas are usually placed in a scenario that’s a” specific context with a problem they want to or have to solve “—does that mean context actually is considered? Unfortunately, what frequently occurs is that you choose a fictional character to play with a particular circumstance based on the fiction. How could you possibly understand how someone you want to represent behave in new circumstances if you hadn’t even fully investigated and understood the current context of the people you want to represent?

    Personas are meaningless averages

    A persona is depicted as a specific person but is not a real person, as stated in Shlomo Goltz’s introduction article on Smashing Magazine; rather, it is synthesized from observations of many people. The famous example of the USA Air Force designing planes based on the average of 140 of their pilots ‘ physical dimensions and not a single pilot actually fit within that average seat is a well-known criticism of this aspect of personas.

    The same limitation applies to mental aspects of people. Have you ever heard a famous person say something was taken out of context? They uttered my words, but I didn’t mean it that way. The celebrity’s statement was reported literally, but the reporter failed to explain the context around the statement and didn’t describe the non-verbal expressions. In the end, the intended meaning was lost. You do the same when you create personas: you collect someone’s statement ( or goal, or need, or emotion ), whose meaning can only be understood if you give its own particular context, and then report it as an isolated finding.

    But personas go a step further, extracting a decontextualized finding and joining it with another decontextualized finding from somebody else. The resultant set of findings frequently does not make sense because it is unclear or even contradictory because it lacks the underlying causes for and how that finding came about. It lacks any significance. And the persona doesn’t give you the full background of the person ( s ) to uncover this meaning: you would need to dive into the raw data for each single persona item to find it. What then is the persona’s usefulness?

    The validity of personas can be deceiving.

    To a certain extent, designers realize that a persona is a lifeless average. Designers create “relatable” personas to make them appear like real people in order to overcome this. Nothing better explains the absurdity of this than a phrase from the Interaction Design Foundation,” Add a few fictional personal details to make the persona a realistic character.” In other words, you add non-realism in an attempt to create more realism. You purposefully understate the fact that” John Doe” is an abstract representation of research findings, but wouldn’t it be much more responsible to emphasize that John is only an abstraction? Let’s say something is artificial, and let’s say it is.

    It’s the finishing touch of a persona’s decontextualization: after having assumed that people’s personalities are fixed, dismissed the importance of their environment, and hidden meaning by joining isolated, non-generalizable findings, designers invent new context to create ( their own ) meaning. They do so by introducing a number of biases, as with everything they create. As designers, as Designit puts it, we can” contextualize]the persona” based on our experience and reality. We create connections that are familiar to us“. With every new detail added, this practice furthers stereotypes, doesn’t reflect real-world diversity, and gets further away from people’s actual reality.

    Everyone should use their own empathy and develop their own interpretation and emotional response if we want to conduct good design research by reporting the reality “as-is” and making it relatable for our audience.

    Dynamic Selves: The alternative to personas

    What should we do instead if we shouldn’t use personas?

    Designit suggests using mindsets rather than personas. Each Mindset is a” spectrum of attitudes and emotional responses that different people have within the same context or life experience”. It challenges designers to avoid becoming fixated on just one person’s way of life. Unfortunately, despite being a step in the right direction, this proposal disregards the fact that people are influenced by how their personality, behavior, and, yes, mindset are shaped by their surroundings. Therefore, Mindsets are also not absolute but change in regard to the situation. What determines a particular Mindset, remains to be seen.

    Another option is provided by Margaret P., the author of the article” Kill Your Personas,” who has argued for replacing personas with persona spectrums that include a range of user abilities. For example, a visual impairment could be permanent ( blindness ), temporary ( recovery from eye surgery ), or situational (screen glare ). Because they recognize that the context is the pattern, not the personality, Persona spectrums are extremely useful for more inclusive and context-based design. However, their only drawback is that they have a very functional perspective on users that misses the relatability of a real person taken from within a spectrum.

    In developing an alternative to personas, we aim to transform the standard design process to be context-based. Similar to how we previously dealt with people, contexts are generalizable and have patterns that we can identify. So how do we learn these patterns? How do we ensure truly context-based design?

    Understand real people in a variety of contexts

    Nothing can be more relatable and inspiring than reality. Therefore, we have to understand real individuals in their multi-faceted contexts, and use this understanding to fuel our design. We refer to this method as Dynamic Selves.

    Let’s take a look at how the approach looks based on an illustration from a recent study that examined Italians ‘ habits around energy consumption. We drafted a design research plan aimed at investigating people’s attitudes toward energy consumption and sustainable behavior, with a focus on smart thermostats.

    1. Select the appropriate sample.

    When we argue against personas, we’re often challenged with quotes such as” Where are you going to find a single person that encapsulates all the information from one of these advanced personas]? ]” The simple answer is that you are not required to. Your insights need not be extensive and meaningful, as you don’t need to know much about everyone.

    In qualitative research, validity does not derive from quantity but from accurate sampling. You pick the people who best fit the “population” you’re designing for. If this sample is chosen wisely and you have a deep understanding of the sampled people, you can infer how the rest of the population thinks and acts. There’s no need to study seven Susans and five Yuriys, one of each will do.

    In fifteen different situations, Susan is not necessary. Once you’ve seen her in a few different settings, you’ve grasped Susan’s general scheme of action. Not Susan as an atomic being but Susan in relation to the surrounding environment: how she might act, feel, and think in different situations.

    It becomes clear why each person should be portrayed as an individual because each already represents an abstraction of a larger group of people in similar circumstances because each person is representative of a portion of the population you’re researching. You don’t want to see abstractions of abstractions! These selected people need to be understood and shown in their full expression, remaining in their microcosmos—and if you want to identify patterns you can focus on identifying patterns in contexts.

    However, the question persists: how do you choose a sample representative? First, you must consider the target market for the product or service you are designing. It might be helpful to examine the company’s objectives and strategy, the current customer base, and/or a potential future target audience.

    In our example project, we were designing an application for those who own a smart thermostat. Everyone in their home could have a smart thermostat in the future. However, only early adopters currently own one. To build a significant sample, we needed to understand the reason why these early adopters became such. We then recruited by enticing customers to explain their needs and sources of purchase. There were those who had made the decision to purchase it, those who had been influenced by others to do so, and those who had located it in their homes. So we selected representatives of these three situations, from different age groups and geographical locations, with an equal balance of tech savvy and non-tech savvy participants.

    2. Conduct your research

    After having chosen and recruited your sample, conduct your research using ethnographic methodologies. Your qualitative data will be enriched with examples and anecdotes thanks to this. Given COVID-19 restrictions, we turned an internal ethnographic research project into home-based remote family interviews that were followed by diary research in our example project.

    To gain an in-depth understanding of attitudes and decision-making trade-offs, the research focus was not limited to the interviewee alone but deliberately included the whole family. Each interviewee would provide a story that would later become much more interesting and precise with the additions made by their spouses, partners, kids, or occasionally even pets. We also paid attention to the behaviors that came from having relationships with other meaningful people ( such as coworkers or distant relatives ) and the relationships that came from those relationships. This wide research focus allowed us to shape a vivid mental image of dynamic situations with multiple actors.

    It’s crucial that the research’s scope remain broad enough to cover all potential actors. Therefore, it typically works best to define broad research areas with broad questions. Interviews are best set up in a semi-structured way, where follow-up questions will dive into topics mentioned spontaneously by the interviewee. The most insightful findings will be made with this open-minded “plan to be surprised.” One of our participants responded to our question about how his family controlled the house temperature by saying,” My wife has not installed the thermostat’s app; she uses WhatsApp instead. If she wants to turn on the heater and she is not home, she will text me. I serve as her thermostat.

    3. Analysis: Create the Dynamic Selves

    You begin to represent each individual with several Dynamic Selves, each” Self” representing one of the circumstances you have examined throughout the research analysis. A quote serves as the foundation of each Dynamic Self, which is supported by a photo and a few relevant demographics that help to illustrate the larger context. The research findings themselves will show which demographics are relevant to show. In our case, the important demographics were family type, number and type of houses owned, economic status, and technological maturity because our research focused on families and their way of life to understand their needs for thermal regulation. To facilitate the stakeholders ‘ transition from personas and be able to connect multiple actions and contexts to the same person, we also included the individual’s name and age, but they are optional.

    To capture exact quotes, interviews need to be video-recorded and notes need to be taken verbatim as much as possible. This is crucial to ensuring that each participant’s various selves are truthful. To create authentic selves in ethnographic research using real-world actors and photos of the setting are necessary. Ideally, these photos should come directly from field research, but an evocative and representative image will work, too, as long as it’s realistic and depicts meaningful actions that you associate with your participants. One of our interviewees, for instance, shared a story of his mountain home where he used to spend weekends with his family. Therefore, we depicted him taking a hike with his young daughter.

    At the end of the research analysis, we displayed all of the Selves ‘” cards” on a single canvas, categorized by activities. A quote and a unique photo were displayed on each card, each illustrating a situation. Each participant had several cards about themselves.

    4. Identify potential designs

    You’ll start to notice patterns when you’ve written down all of the key phrases from the interview transcripts and diaries as self-cards. These patterns will highlight the opportunity areas for new product creation, new functionalities, and new services—for new design.

    A particularly intriguing finding was made in our example project regarding the concept of humidity. We became aware of the importance of monitoring humidity for health and that people don’t know what it is because an environment that’s too dry or wet can cause respiratory problems or worsen already existing ones. This highlighted a big opportunity for our client to educate users on this concept and become a health advisor.

    Benefits of Dynamic Selves

    When you conduct your research using the Dynamic Selves method, you start to notice peculiar social relations, peculiar circumstances that people face, and the consequences of their actions, as well as the fact that people are surrounded by constantly changing environments. In our thermostat project, we have come to know one of the participants, Davide, as a boyfriend, dog-lover, and tech enthusiast.

    Davide is a person we might have once consigned to the persona of a “tech enthusiast.” However, there are also those who love technology who have families or are single, who are wealthy or poor. Their motivations and priorities when deciding to purchase a new thermostat can be opposite according to these different frames.

    You can generalize how he would act in a different situation once you have understood Davide in more detail and have fully understood the underlying causes of his behavior for each circumstance. You can infer what he would think and do in the circumstances ( or scenarios ) you design for using your understanding of him.

    The Dynamic Selves approach aims to dismiss the conflicted dual purpose of personas—to summarize and empathize at the same time—by separating your research summary from the people you’re seeking to empathize with. This is crucial because scale affects how we feel empathy for people and how difficult it is to do so with other people. We have the deepest sympathy for people who are able to relate to us.

    If you take a real person as inspiration for your design, you no longer need to create an artificial character. No more developing plot devices to “realize” the character, and no more need for additional bias. This is exactly how this person lives out. In fact, in our experience, personas quickly become nothing more than a name in our priority guides and prototype screens, as we all know that these characters don’t really exist.

    Another significant benefit of Dynamic Selves is that it raises the stakes of your work: if you ruin your design, someone you and the team know and have met will suffer the consequences. It might prompt you to perform daily design checks and may prevent you from taking shortcuts.

    And finally, real people in their specific contexts are a better basis for anecdotal storytelling and therefore are more effective in persuasion. Real research documentation is necessary to obtain this result. It reinforces your design arguments by adding more weight and urgency:” When I met Alessandra, the conditions of her workplace struck me. Noise, bad ergonomics, lack of light, you name it. I’m worried that her life will become more complicated if we choose to use this functionality.

    Conclusion

    Designit stated in their article on Mindsets that “design thinking tools offer a shortcut to deal with reality’s complexities, but this process of simplification can occasionally flatten out people’s lives into a few general characteristics.” Unfortunately, personas have been culprits in a crime of oversimplification. They fail to account for the complex nature of our users ‘ decision-making processes and don’t take into account the fact that people are immersed in contexts.

    Design needs to be simplified, not necessarily generalized. You have to look at the research elements that stand out: the sentences that captured your attention, the images that struck you, the sounds that linger. Use those as metaphors for the person in all of their contexts. People and insights are subject to a context, but they cannot be removed because it would detract from the context’s meaning.

    It’s high time for design to move away from fiction, and embrace reality—in its messy, surprising, and unquantifiable beauty—as our guide and inspiration.

  • Designing for the Unexpected

    Designing for the Unexpected

    Although I’m not certain when I first heard this statement, it has stuck with me over the centuries. How do you generate solutions for scenarios you can’t think? Or create materials that are functional on products that have not yet been created?

    Flash, Photoshop, and flexible style

    My go-to program when I first started designing platforms was Photoshop. I created a 960px paint and set about creating a design that I would eventually lose information in. The growth phase aimed to achieve pixel-perfect accuracy by using set widths, fixed heights, and absolute setting.

    Ethan Marcotte’s speak at An Event Off and subsequent content” Responsive Web Design” in A List Off in 2010 changed all this. As soon as I learned about flexible style, I was convinced, but I was even terrified. The pixel-perfect models full of special figures that I had formerly prided myself on producing were no longer good enough.

    My first encounter with reactive style didn’t help my fear. My second project was to get an active fixed-width website and make it reactive. I quickly realized that you didn’t just put responsiveness at the end of a job. To make smooth design, you need to prepare throughout the style phase.

    A novel method of style

    Developing flexible or smooth sites has always been about removing limitations, producing material that can be viewed on any system. It relies on using percentage-based layouts, which I immediately achieved using native CSS and power courses:

    .column-span-6 { width: 49%; float: left; margin-right: 0.5%; margin-left: 0.5%;}.column-span-4 { width: 32%; float: left; margin-right: 0.5%; margin-left: 0.5%;}.column-span-3 { width: 24%; float: left; margin-right: 0.5%; margin-left: 0.5%;}

    Then with Sass so I could take advantage of @includes to re-use repeated slabs of script and walk up to more semantic premium:

    .logo { @include colSpan(6);}.search { @include colSpan(3);}.social-share { @include colSpan(3);}

    internet inquiries

    The next ingredient for reactive design is press queries. Without them, regardless of whether the information was still readable, may shrink to fit the available storage.

    internet inquiries prevented this by allowing us to add breakpoints where the design could adapt. Like most people, I started out with three breakpoints: one for desktop, one for tablets, and one for mobile. Over the years, I added more and more for phablets, wide screens, and so on. 

    For years, I happily worked this way and improved both my design and front-end skills in the process. The only problem I encountered was making changes to content, since with our Sass grid system in place, there was no way for the site owners to add content without amending the markup—something a small business owner might struggle with. This is because each row in the grid was defined using a div as a container. Adding content meant creating new row markup, which requires a level of HTML knowledge.

    String premium was a mainstay of early flexible design, present in all the frequently used systems like Bootstrap and Skeleton.

    1 of 7
    2 of 7
    3 of 7
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    5 of 7
    6 of 7
    7 of 7

    Another difficulty arose as I moved from a design firm building websites for smaller- to medium-sized companies, to larger in-house teams where I worked across a collection of related sites. In those capacities, I began to work many more with washable pieces.

    Our rely on multimedia queries resulted in parts that were tied to frequent screen sizes. If modify is the goal of part libraries, then this is a real issue because you can just use these components if the devices you’re designing for match the window sizes in the design library, which prevents you from actually achieving the “devices that don’t already exist” goal.

    Then there’s the problem of space. internet inquiries allow components to adapt based on the viewport size, but what if I put a component into a sidebar, like in the figure below?

    Container queries: a false dawn or our lord?

    Container concerns have long been touted as an improvement upon advertising questions, but at the time of writing are unsupported in most computers. Workarounds for JavaScript exist, but they can lead to dependencies and compatibility issues. The basic theory underlying container queries is that elements should change based on the size of their parent container and not the viewport width, as seen in the following illustrations.

    One of the biggest arguments in favor of container queries is that they help us create components or design patterns that are truly reusable because they can be picked up and placed anywhere in a layout. This is a significant step in the direction of a component-based design that can be used on any device of any size.

    In other words, responsive components to replace responsive layouts.

    Container queries will enable us to design components that can be placed in a sidebar or in the main content rather than pages that respond to the browser or device size.

    My concern is that we are still using layout to determine when a design needs to adapt. This approach will always be restrictive because we will still require predetermined breakpoints. For this reason, my main question with container queries is, How would we decide when to change the CSS used by a component?

    A component library that is disconnected from context and real content is probably not the best place to make that choice.

    As the diagrams below illustrate, we can use container queries to create designs for specific container widths, but what if I want to change the design based on the image size or ratio?

    The container’s dimensions shouldn’t be the design’s, but rather the image should.

    It’s hard to say for sure whether container queries will be a success story until we have solid cross-browser support for them. Responsive component libraries would undoubtedly change the way we design, and they would increase the possibilities for reuse and design at scale. But maybe we will always need to adjust these components to suit our content.

    CSS is evolving.

    Whilst the container query debate rumbles on, there have been numerous advances in CSS that change the way we think about design. The days of fixed-width elements measured in pixels and floated div elements used to cobble layouts together are long gone, consigned to history along with table layouts. Flexbox and CSS Grid have revolutionized layouts for the web. We can now create elements that wrap onto new rows when they run out of space, not when the device changes.

    .wrapper { display: grid; grid-template-columns: repeat(auto-fit, 450px); gap: 10px;}

    The repeat() function paired with auto-fit or auto-fill allows us to specify how much space each column should use while leaving it up to the browser to decide when to spill the columns onto a new line. Similar things can be achieved with Flexbox, as elements can wrap over multiple rows and “flex” to fill available space. 

    .wrapper { display: flex; flex-wrap: wrap; justify-content: space-between;}.child { flex-basis: 32%; margin-bottom: 20px;}

    The biggest benefit of all this is you don’t need to wrap elements in container rows. Without rows, content is not tied to page markup in the same way, allowing for changes or additions to content without further development.

    This is a significant improvement when it comes to developing designs that allow for dynamic content, but CSS Subgrid is the real game changer for flexible designs.

    Remember the days of crafting perfectly aligned interfaces, only for the customer to add an unbelievably long header almost as soon as they’re given CMS access, like the illustration below?

    Subgrid allows elements to respond to adjustments in their own content and in the content of sibling elements, helping us create designs more resilient to change.

    .wrapper { display: grid; grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); grid-template-rows: auto 1fr auto; gap: 10px;}.sub-grid { display: grid; grid-row: span 3; grid-template-rows: subgrid; /* sets rows to parent grid */}

    CSS Grid allows us to separate layout and content, thereby enabling flexible designs. Subgrid, in contrast, allows us to create designs that can be modified to fit changing content. Subgrid at the time of writing is only supported in Firefox but the above code can be implemented behind an @supports feature query.

    Intrinsic layouts

    I’d be remiss not to mention intrinsic layouts, the term created by Jen Simmons to describe a mixture of new and old CSS features used to create layouts that respond to available space.

    Columns with percentages are flexible in responsive layouts. Intrinsic layouts, on the other hand, use the fr unit to create flexible columns that won’t ever shrink so much that they render the content illegible.

    frunits is a statement that says,” I want you to distribute the extra space in this way, but… don’t ever make it smaller than the content that is inside of it.”

    —Jen Simmons,” Designing Intrinsic Layouts”

    Intrinsic layouts can also make use of a mix of fixed and flexible units, letting the content choose how much space it occupies.

    What distinguishes intrinsic design is that it not only creates designs that can withstand future devices but also helps scale designs without losing flexibility. Components and patterns can be lifted and reused without the prerequisite of having the same breakpoints or the same amount of content as in the previous implementation.

    We can now make designs that work in harmony with the content inside and the content around them. With an intrinsic approach, we can construct responsive components without depending on container queries.

    Another 2010 moment?

    This intrinsic approach should in my view be every bit as groundbreaking as responsive web design was ten years ago. It’s another “everything changed” moment for me.

    But it doesn’t seem to be moving quite as fast, I haven’t yet had that same career-changing moment I had with responsive design, despite the widely shared and brilliant talk that brought it to my attention.

    One possible explanation for that is that I now work for a sizable company, which is quite different from the design agency position I held in 2010. In my agency days, every new project was a clean slate, a chance to try something new. Modern projects frequently improve existing websites with an existing codebase and use existing tools and frameworks.

    Another could be that I feel more prepared for change now. I was brand-new to design in general in 2010, and the shift involved a lot of learning. Also, an intrinsic approach isn’t exactly all-new, it’s about using existing skills and existing CSS knowledge in a different way.

    You can’t framework your way out of a content issue.

    Another reason for the slightly slower adoption of intrinsic design could be the lack of quick-fix framework solutions available to kick-start the change.

    Ten years ago, responsive grid systems were everywhere. With a framework like Bootstrap or Skeleton, you had a responsive design template at your fingertips.

    Because having a selection of units is a hindrance when creating layout templates, intrinsic design and frameworks do not work together quite as well. The beauty of intrinsic design is combining different units and experimenting with techniques to get the best for your content.

    There are also design tools. We probably all, at some point in our careers, used Photoshop templates for desktop, tablet, and mobile devices to drop designs in and show how the site would look at all three stages.

    How do you do that right away, with each component reacting to content and layout flexing as needed? This type of design must happen in the browser, which personally I’m a big fan of.

    Another topic that has persisted for years is the debate over whether designers should code. When designing a digital product, we should, at the very least, design for a best- and worst-case scenario when it comes to content. It’s not ideal to do this in a graphics-based software package. In code, we can add longer sentences, more radio buttons, and extra tabs, and watch in real time as the design adapts. Does it continue to function? Is the design too reliant on the current content?

    Personally, I look forward to the day that a design component can truly be flexible and adapt to both its space and content without relying on the device or container dimensions. This is the day intrinsic design is the standard for design.

    Content first

    Content is not a fixed thing. After all, to design for the unknown or unexpected we need to account for content changes like our earlier Subgrid card example that allowed the cards to respond to adjustments to their own content and the content of sibling elements.

    Thankfully, there’s more to CSS than layout, and plenty of properties and values can help us put content first. Subgrid and pseudo-elements like ::first-line and ::first-letter help to separate design from markup so we can create designs that allow for changes.

    Instead of the dated markup tricks below,

    First line of text with different styling...

    —we can target content based on where it appears.

    .element::first-line { font-size: 1.4em;}.element::first-letter { color: red;}

    Much bigger additions to CSS include logical properties, which change the way we construct designs using logical dimensions (start and end) instead of physical ones (left and right), something CSS Grid also does with functions like min(), max(), and clamp().

    This flexibility allows for directional changes according to content, a common requirement when we need to present content in multiple languages. This was frequently accomplished with Sass mixins in the past, but it was frequently limited to switching from left-to-right to right-to-left orientation.

    In the Sass version, directional variables need to be set.

    $direction: rtl;$opposite-direction: ltr;$start-direction: right;$end-direction: left;

    These variables can also be used as values—

    body { direction: $direction; text-align: $start-direction;}

    —or as properties.

    margin-#{$end-direction}: 10px;padding-#{$start-direction}: 10px;

    However, with native logical properties, there is no longer a need to rely on Sass ( or another similar tool ) or pre-planning, which made using variables throughout a codebase necessary. These properties also start to break apart the tight coupling between a design and strict physical dimensions, creating more flexibility for changes in language and in direction.

    margin-block-end: 10px;padding-block-start: 10px;

    There are also native start and end values for properties like text-align, which means we can replace text-align: right with text-align: start.

    Like the earlier examples, these properties help to build out designs that aren’t constrained to one language, the design will reflect the content’s needs.

    Fluid and fixed

    We briefly covered the power of combining fixed widths with fluid widths with intrinsic layouts. The min() and max() functions are a similar concept, allowing you to specify a fixed value with a flexible alternative. 

    For min() this means setting a fluid minimum value and a maximum fixed value.

    .element { width: min(50%, 300px);}

    As long as the element’s width is not greater than 300px, the element in the figure above will cover 50 % of its container.

    For max() we can set a flexible max value and a minimum fixed value.

    .element { width: max(50%, 300px);}

    As long as the element’s width is at least 300px, the element will now be 50 % of its container. This means we can set limits but allow content to react to the available space.

    The clamp() function builds on this by allowing us to set a preferred value with a third parameter. Now we can allow the element to shrink or grow if it needs to without getting to a point where it becomes unusable.

    .element { width: clamp(300px, 50%, 600px);}

    This time, the element’s width will be 50 % ( the preferred value ) of its container, with no exceptions for 300px and 600px.

    With these techniques, we have a content-first approach to responsive design. We can distinguish between markup and content, which means that user modifications will not have an impact on the design. We can start to future-proof designs by planning for unexpected changes in language or direction. Additionally, we can increase flexibility by enabling more or less content to be displayed correctly by matching desired dimensions with adaptable alternatives.

    Situation first

    We can address device flexibility by changing our approach, which focuses on content and space rather than devices, as we’ve discussed so far. But what about that last bit of Jeffrey Zeldman’s quote,”… situations you haven’t imagined”?

    It’s a lot different to design for someone using a mobile phone and walking through a crowded street in glaring sunshine than it is for someone using a desktop computer. Situations and environments are hard to plan for or predict because they change as people react to their own unique challenges and tasks.

    Choice is so crucial because of this. One size never fits all, so we need to design for multiple scenarios to create equal experiences for all our users.

    Thankfully, we have many options available to you.

    Responsible design

    There are places in the world where mobile data is prohibitively expensive and where there is little or no broadband infrastructure.

    I Used the Web for a Day on a 50 MB Budget

    Chris Ashton

    One of the biggest assumptions we make is that people interacting with our designs have a good wifi connection and a wide screen monitor. However, in the real world, our users may be commuters using smaller mobile devices that may experience drops in connectivity while traveling on trains or other modes of transportation. There is nothing more frustrating than a web page that won’t load, but there are ways we can help users use less data or deal with sporadic connectivity.

    The srcset attribute allows the browser to decide which image to serve. This means we can create smaller ‘cropped’ images to display on mobile devices in turn using less bandwidth and less data.

    Image alt text

    The preload attribute can also help us to think about how and when media is downloaded. It can be used to tell a browser about any critical assets that need to be downloaded with high priority, improving perceived performance and the user experience. 

      

    Additionally, there is native lazy loading, which indicates that assets should only be downloaded when they are required.

    …

    With srcset, preload, and lazy loading, we can start to tailor a user’s experience based on the situation they find themselves in. What none of this does, however, is allow the user themselves to decide what they want downloaded, as the decision is usually the browser’s to make. 

    So how can we put users in control?

    The return of media inquiries

    internet inquiries have always been about much more than device sizes. They allow content to adapt to different situations, with screen size being just one of them.

    We’ve long been able to check for media types like print and speech and features such as hover, resolution, and color. Because of these checks, we can offer options that work for more than one situation. It’s less about one-size-fits-all and more about providing adaptable content.

    As of this writing, the Media Queries Level 5 spec is still under development. It brings up some really intriguing queries that will eventually enable us to design for a number of other unanticipated situations.

    For example, there’s a light-level feature that allows you to modify styles if a user is in sunlight or darkness. These features, which have custom properties, make it simple to create designs or themes for particular environments.

    @media (light-level: normal) { --background-color: #fff; --text-color: #0b0c0c; }@media (light-level: dim) { --background-color: #efd226; --text-color: #0b0c0c;}

    Another key feature of the Level 5 spec is personalization. Instead of creating designs that are the same for everyone, users can choose what works for them. This is achieved by using features like prefers-reduced-data, prefers-color-scheme, and prefers-reduced-motion, the latter two of which already enjoy broad browser support. These features tap into preferences set via the operating system or browser so people don’t have to spend time making each site they visit more usable. 

    internet inquiries like this go beyond choices made by a browser to grant more control to the user.

    Expect the unanticipated

    In the end, the one thing we should always expect is for things to change. With foldable screens already available, especially in the form of tablets, we can’t keep up with them.

    We can’t design the same way we have for this ever-changing landscape, but we can design for content. We can create more robust, flexible designs that increase the longevity of our products by putting content first and allowing that content to adapt to whatever space surrounds it.

    A lot of the CSS discussed here is about moving away from layouts and putting content at the heart of design. There are still many more things we can do to adopt a more intrinsic approach, from responsive to fluid and fixed. Even better, we can test these techniques during the design phase by designing in-browser and watching how our designs adapt in real-time.

    When it comes to unexpected circumstances, we must make sure our goods are accessible whenever and wherever needed. We can move closer to achieving this by involving users in our design decisions, by creating choice via browsers, and by giving control to our users with user-preference-based media queries.

    Unexpected design should give our users, who we serve, choice and control over how they interact with the environment.

  • Asynchronous Design Critique: Getting Feedback

    Asynchronous Design Critique: Getting Feedback

    ” Any opinion”? is perhaps one of the worst ways to ask for suggestions. It’s obscure and unfocused, and it doesn’t give a clear picture of what we’re looking for. Great feedback begins sooner than we might anticipate: it begins with the request.

    It might seem contradictory to start the process of receiving feedback with a problem, but that makes sense if we realize that getting feedback can be thought of as a form of pattern research. The best way to ask for feedback is to write strong issues, just like we wouldn’t do any studies without the right questions to get the insight we need.

    Design analysis is not a one-time procedure. Sure, any great comments process continues until the project is finished, but this is especially true for layout because architecture work continues iteration after iteration, from a high level to the finest details. Each stage requires its unique set of questions.

    Finally, we need to review what we received, get to the heart of its insight, and taking action, like with any good research. Problem, generation, and evaluation. Let’s take a closer look at each of those.

    The query

    Being available to input is important, but we need to be specific about what we’re looking for. Any comments,” What do you think,” or” I’d love to hear your mind” at the conclusion of a presentation are likely to garner a lot of different ideas, or worse, to make everyone follow the lead of the first speaker. And finally, we become irritated because ambiguous queries like those can result in people leaving reviews that don’t even consider keys. Which might be a savory matter, so it might be hard at that point to divert the crew to the topics that you had wanted to focus on.

    But how do we enter this circumstance? It’s a combination of various components. One is that we don’t often consider asking as a part of the input approach. Another is how healthy it is to keep the issue open and assume that everyone else will agree. Another is that being extremely detailed is frequently not necessary in non-professional conversations. In short, we tend to underestimate the importance of the issues, so we don’t work on improving them.

    The practice of asking nice questions guidelines and concentrates the criticism. It also serves as a form of acceptance, outlining your willingness to make comments and the types of responses you want to receive. It puts people in the right emotional state, especially in situations when they weren’t expecting to give opinions.

    There isn’t a second best way to ask for opinions. Sensitivity can take countless forms, and it just needs to be that. A design for design critique that I’ve found especially helpful in my training is the one of stage over depth.

    The term” level” refers to each of the stages of the process, in our case, the design phase. The type of input changes as the consumer research moves forward to the final design. But within a single stage, one might also examine whether some assumptions are correct and whether there’s been a suitable language of the amassed input into updated designs as the job has evolved. The levels of customer experience may serve as a starting point for future inquiries. What are the project targets, in your opinion? User requirements? Funnality? the glad Contact design? a system of information layout Interface style Navigation style? physical architecture Brand?

    Here’re a some example questions that are specific and to the place that refer to different levels:

    • Functionality: Is it attractive to automate accounts creation?
    • Contact design: Please review the updated flow for any errors or steps I might have missed.
    • Information infrastructure: We have two competing bits of information on this site. Does the construction work to effectively communicate both of them?
    • User interface design: What do you think about the top-of-the-page problem counter, which makes sure you can see the following error even when the error is outside the viewport?
    • Navigation style: From study, we identified these second-level routing items, but when you’re on the webpage, the list feels very long and hard to understand. Exist any recommendations for resolving this?
    • Are the thick alerts in the bottom-right corner of the page visible enough?

    The other plane of sensitivity is about how heavy you’d like to go on what’s being presented. For instance, we may have introduced a new end-to-end movement, but you might want to know more about a particular viewpoint you found challenging. This can be especially helpful when switching between iterations because it’s crucial to identify the changes made.

    There are other issues that we can consider when we want to accomplish more specific—and more effective—questions.

    Eliminating generic finals from your questions like “good,” “well,” “nice,” “bad,” “okay,” and” cool” is a simple strategy. Asking,” When the stop opens and the switches appear, is this conversation excellent, for instance?” may seem precise, but you can place the “good” tournament, and transfer it to an even better query:” When the wall opens and the buttons appear, is it clear what the next action is”?

    Sometimes we do want a lot of feedback. Although that is uncommon, it is possible. In that sense, you might still make it explicit that you’re looking for a wide range of opinions, whether at a high level or with details. Or perhaps just say,” At first glance, what do you think”? so that it is obvious that what you’re asking is open ended but focused on a person’s impression after their first five seconds of inquiry.

    Sometimes the project is particularly expansive, and some areas may have already been explored in detail. In these circumstances, it might be helpful to state explicitly that some parts are already locked in and aren’t accessible for feedback. Although it’s not something I’d recommend in general, I’ve found it helpful in avoiding getting back into rabbit holes like those that could lead to even more refinement if what’s important right now isn’t.

    Asking specific questions can completely change the quality of the feedback that you receive. People who have less refined critique abilities will now be able to provide more useful feedback, and even experienced designers will appreciate the clarity and effectiveness gained from concentrating solely on what is required. It can save a lot of time and frustration.

    The iteration

    Design iterations are probably the most recognizable component of the design process, and they act as a natural feedback loop. Many design tools have inline commenting, but many of them only display changes as a single fluid stream in the same file. In addition, these kinds of design tools automatically update shared UI components, make conversations disappear and require designs to always display the most recent version, unless these would-be useful features were manually disabled. The implied goal that these design tools seem to have is to arrive at just one final copy with all discussions closed, probably because they inherited patterns from how written documents are collaboratively edited. That’s probably not the most effective way to go about designing critiques, but even if I don’t want to be too prescriptive, it might work for some teams.

    The asynchronous design-critique approach that I find most effective is to make explicit checkpoints for discussion. I’m going to use the term iteration post for this. It refers to a write-up or presentation of the design iteration that is followed by some sort of discussion thread. Any platform that can accommodate this type of structure can use this. By the way, when I refer to a “write-up or presentation“, I’m including video recordings or other media too: as long as it’s asynchronous, it works.

    There are many benefits to using iteration posts:

      It establishes a rhythm in the design process, allowing the designer to review the feedback from each iteration and get ready for the following.
    • It makes decisions visible for future review, and conversations are likewise always available.
    • It keeps track of how the design evolved over time.
    • It might also make it simpler to collect and act on feedback depending on the tool.

    These posts of course don’t mean that no other feedback approach should be used, just that iteration posts could be the primary rhythm for a remote design team to use. And from there, other feedback techniques ( such as live critique, pair designing, or inline comments ) can emerge.

    There isn’t, in my opinion, a universal format for iteration posts. But there are a few high-level elements that make sense to include as a baseline:

    1. The objective is.
    2. The layout
    3. The list of changes
    4. The querys

    A goal for each project is likely to be one that has already been condensed into a single sentence, such as the request for the project owner, the product manager, or the client brief. So this is something that I’d repeat in every iteration post—literally copy and pasting it. To avoid having to search through information from multiple posts, the goal is to provide context and repeat what is necessary to complete each iteration post. The most recent iteration post will provide all I need to know about the most recent design.

    This copy-and-paste part introduces another relevant concept: alignment comes from repetition. Therefore, repeating information in posts helps to ensure that everyone is on the same page.

    The actual series of information-architecture outlines, diagrams, flows, maps, wireframes, screens, visuals, and any other design work that has been done is what the design is then called. In short, it’s any design artifact. In the work’s final stages, I prefer to use the term “blank” to emphasize that I’ll be displaying complete flows rather than individual screens to facilitate comprehension of the larger picture.

    It might also be helpful to have clear names on the objects since it makes them look better to refer to. Write the post in a way that helps people understand the work. It’s not very different from creating a strong live presentation.

    A bullet list of the changes made in the previous iteration should also be included for an effective discussion so that attendees can concentrate on what’s changed. This can be especially useful for larger works of work where keeping track, iteration after iteration, might prove difficult.

    And finally, as noted earlier, it’s essential that you include a list of the questions to drive the design critique in the direction you want. Making a numbered list of questions available in the form of a number can also make it simpler to refer to each one by its name.

    Not every iteration is the same. Earlier iterations don’t need to be as tightly focused—they can be more exploratory and experimental, maybe even breaking some of the design-language guidelines to see what’s possible. Then, later, the iterations begin coming to a decision and improving it until the design process is complete and the feature is ready.

    Even if these iteration posts are written and intended as checkpoints, I want to point out that they are not by any means required to be exhaustive. A post might be a draft—just a concept to get a conversation going—or it could be a cumulative list of each feature that was added over the course of each iteration until the full picture is done.

    I also started using specific labels for incremental iterations over time: i1, i2, i3, and so on. Although this may seem like a minor labeling tip, it can be useful in many ways:

    • Unique—It’s a clear unique marker. Everyone knows where to go to review things, and it’s simple to say” This was discussed in i4″ with each project.
    • Unassuming—It functions like versions ( such as v1, v2, and v3 ), but versions give the impression of something that is large, exhaustive, and complete. Iterations must be able to be exploratory, incomplete, partial.
    • Future proof—It resolves the “final” naming issue that versions can have. No more files with the title “final final complete no-really-its-done” Within each project, the largest number always represents the latest iteration.

    The wording release candidate (RC ) could be used to indicate when a design is finished enough to be worked on, even if there are some areas that still need improvement and, in turn, require more iterations, such as” with i8 we reached RC” or “i12 is an RC” to indicate when it is finished.

    The evaluation

    What usually happens during a design critique is an open discussion, with a back and forth between people that can be very productive. This strategy is particularly successful when synchronous feedback is being received live. However, when we work asynchronously, using a different approach is more effective: we can adopt a user-research mindset. Written feedback from teammates, stakeholders, or others can be treated as if it were the result of user interviews and surveys, and we can analyze it accordingly.

    This shift has some significant advantages, making asynchronous feedback particularly effective, especially around these friction points:

      It lessens the need to respond to everyone.
    1. It reduces the frustration from swoop-by comments.
    2. It lowers the stakes we have in ourselves.

    The first friction point is having to press yourself to respond to each and every comment. Sometimes we write the iteration post, and we get replies from our team. It’s just a few of them, it’s simple, and there isn’t much of a problem with it. Sometimes, however, some solutions may require more in-depth discussions, and the number of responses can quickly rise, which can cause tension between trying to be a good team player by responding to everyone and attempting the next design iteration. This might be especially true if the person who’s replying is a stakeholder or someone directly involved in the project who we feel that we need to listen to. It’s human nature to try to accommodate those we care about, and we need to accept that this pressure is completely normal. When responding to all comments, it can be effective, but when we consider a design critique more like user research, we realize that we don’t need to respond to every comment, and there are alternatives in asynchronous spaces:

      One is to let the next iteration speak for itself. When the design changes and we publish a follow-up iteration, that’s the response. You could tag everyone in the previous discussion, but even that is a choice, not a requirement.
    • Another is to briefly reply to acknowledge each comment, such as” Understood. Thank you,”” Good points— I’ll review,” or” Thanks. These will be included in the upcoming iteration. In some cases, this could also be just a single top-level comment along the lines of” Thanks for all the feedback everyone—the next iteration is coming soon”!
    • One more thing is to quickly summarize the comments before proceeding. This may be particularly helpful if your workflow allows you to create a simplified checklist that you can use for the following iteration.

    The second friction point is the swoop-by comment, which is the kind of feedback that comes from someone outside the project or team who might not be aware of the context, restrictions, decisions, or requirements —or of the previous iterations ‘ discussions. One can hope that they will learn something from them, starting with acknowledging that they are doing this and making their location more explicit. It can be annoying to have to repeat the same response repeatedly in swoop-by comments.

    Let’s begin by acknowledging again that there’s no need to reply to every comment. However, if responding to a previously litigated point is useful, a brief response with a link to the previous discussion for additional information is typically sufficient. Remember that repetition results in alignment; therefore, it’s acceptable to occasionally repeat things!

    Swoop-by commenting can still be useful for two reasons: they might point out something that still isn’t clear, and they also have the potential to stand in for the point of view of a user who’s seeing the design for the first time. Yes, you’ll still be frustrated, but that might at least make things better for you.

    The personal stake we might have in relation to the design could be the third friction point, which might cause us to feel defensive if the review turned out to be more of a discussion. Treating feedback as user research helps us create a healthy distance between the people giving us feedback and our ego ( because yes, even if we don’t want to admit it, it’s there ). And in the end, presenting everything in aggregated form helps us to prioritize our work more.

    Remember to always remember that you don’t have to accept every piece of feedback, even though you need to listen to stakeholders, project owners, and specific advice. You have to analyze it and make a decision that you can justify, but sometimes “no” is the right answer.

    You are in charge of making that choice as the project designer. In the end, everyone has their area of specialization, and the designer is the one with the most background and knowledge to make the right choice. And by listening to the feedback that you’ve received, you’re making sure that it’s also the best and most balanced decision.

    Thanks to Mike Shelton and Brie Anne Demkiw for their contributions to the initial draft of this article.

  • Asynchronous Design Critique: Giving Feedback

    Asynchronous Design Critique: Giving Feedback

    One of the most powerful smooth abilities we have at our disposal is the ability to work together to improve our designs while developing our own abilities and perspectives, regardless of how it is used or what it might be called.

    Feedback is also one of the most underestimated equipment, and generally by assuming that we’re already good at it, we settle, forgetting that it’s a talent that can be trained, grown, and improved. Bad opinions can lead to conflict in projects, lower morale, and long-term, undermine trust and teamwork. Quality comments can be a revolutionary force.

    Practicing our knowledge is absolutely a good way to enhance, but the learning gets yet faster when it’s paired with a good base that programs and focuses the exercise. What are some fundamental components of providing effective opinions? And how can suggestions be changed for isolated and distributed workplaces?

    On the web, we may discover a long history of sequential suggestions: from the early weeks of open source, script was shared and discussed on email addresses. Developers and sprint masters discuss ideas on tickets, designers make comments in their favourite design tools, and so on.

    Design analysis is frequently used as a term for a type of collaborative feedback that is provided to improve our work. So it shares a lot of the rules with comments in public, but it also has some variations.

    The information

    The content of the feedback is the basis of every effective criticism, so where do we need to begin? There are many versions that you can use to design your content. This one from Lara Hogan is the one I personally like best because it’s obvious and actionable.

    This calculation, which is typically used to provide feedback to users, even fits really well in a design critique because it finally addresses one of the main issues that we address: What? Where? Why? How? Imagine that you’re giving some comments about some pattern function that spans several screens, like an onboard movement: there are some pages shown, a stream blueprint, and an outline of the decisions made. You notice anything that needs to be improved. You’ll have a mental design that will enable you to get more accurate and effective if you keep in mind the three components of the equation.

    Here is a reply that could be given as a part of some comments, and it might seem reasonable at a first glimpse: it seems to casually serve the elements in the equation. Does it, though?

    Not sure about the hierarchy and styles of the buttons; it seems off. Can you change them?

    Finding a perspective that is as specific as possible when conducting design feedback refers to more than just pointing out which area of the interface. Do you offer the user’s viewpoint? Your expert perspective? from a business perspective? From the perspective of the project manager? A first-time user’s perspective?

    When I see these two buttons, I anticipate one to go forward and the other to go back.

    Impact is about the why. Just pointing out a UI element might sometimes be enough if the issue may be obvious, but more often than not, you should add an explanation of what you’re pointing out.

    When I see these two buttons, I anticipate one to go forward and the other to go back. But this is the only screen where this happens, as before we just used a single button and an “×” to close. This seems to be breaking the consistency in the flow.

    The question approach is intended to give open guidance by encouraging the designer to think critically while receiving the feedback. Notably, Lara’s equation includes a second approach: request, which instead provides instructions on how to find a particular solution. While that’s a viable option for feedback in general, for design critiques, in my experience, defaulting to the question approach usually reaches the best solutions because designers are generally more comfortable in being given an open space to explore.

    For the question approach, consider the difference between the two:

    When I see these two buttons, I anticipate one to go forward and the other to go back. But this is the only screen where this happens, as before we just used a single button and an “×” to close. This seems to be breaking the consistency in the flow. Would it make sense to unify them?

    Or, for the request approach:

    When I see these two buttons, I anticipate one to go forward and the other to go back. But this is the only screen where this happens, as before we just used a single button and an “×” to close. This seems to be breaking the consistency in the flow. Let’s make sure that all screens have the same pair of forward and back buttons.

    In some situations, it might be helpful to include an additional reason why you think the suggestion is better at this point.

    When I see these two buttons, I anticipate one to go forward and the other to go back. But this is the only screen where this happens, as before we just used a single button and an “×” to close. This seems to be breaking the consistency in the flow. Let’s make sure that all screens have the same two forward and back buttons so that users don’t get confused.

    Choosing between the request and question approaches can occasionally be a matter of personal preference. I spent a while working on improving my feedback, conducting anonymous feedback reviews and sharing feedback with others. After a few rounds of this work and a year later, I got a positive response: my feedback came across as effective and grounded. until I switched teams. Surprise surprise, one particular person gave me a lot of negative feedback. The reason is that I had previously tried not to be prescriptive in my advice—because the people who I was previously working with preferred the open-ended question format over the request style of suggestions. However, there was one person in this other team who now preferred specific guidance. So I changed my feedback so that it included requests.

    One comment that I heard come up a few times is that this kind of feedback is quite long, and it doesn’t seem very efficient. Yes, but no. Let’s look at both sides.

    No, this style of feedback is actually efficient because the length here is a byproduct of clarity, and spending time giving this kind of feedback can provide exactly enough information for a good fix. Additionally, if we zoom out, it may lessen misunderstandings and back-and-forth conversations in the future, thereby increasing overall effectiveness and efficiency of collaboration beyond the single comment. Consider the example above where the feedback would be simply” Let’s make sure that all screens have the same two forward and back buttons.” The designer receiving this feedback wouldn’t have much to go by, so they might just apply the change. The interface might change in later iterations or new features might be introduced, and perhaps the change won’t make sense anymore. Without explaining the why, the designer might assume that the change is one of consistency, but what if it wasn’t? So there could now be an underlying concern that changing the buttons would be perceived as a regression.

    Yes, this type of feedback is not always effective because some comments don’t always need to be thorough, some times because some changes are made because they don’t always follow our instructions, and others because the team may have extensive internal knowledge, which makes some of the whys possible be implied.

    Therefore, the above equation serves as a mnemonic to reflect and enhance the practice rather than a strict template for feedback. Even after years of active work on my critiques, I still from time to time go back to this formula and reflect on whether what I just wrote is effective.

    The atmosphere

    The foundation of feedback is well-rounded content, but that’s not really enough. The soft skills of the person who’s providing the critique can multiply the likelihood that the feedback will be well received and understood. It has been demonstrated that only positive feedback can lead to lasting change in people, and tone alone can determine whether content is rejected or welcomed.

    Tone is crucial to work on because our goal is to be understood and to have a positive working environment. Over the years, I’ve tried to summarize the required soft skills in a formula that mirrors the one for content: the receptivity equation.

    Respectful feedback comes across as grounded, solid, and constructive. It’s the kind of feedback that, regardless of whether it’s positive or negative, is viewed as useful and fair.

    Timing refers to when the feedback happens. When given at the wrong time, to-the-point feedback has little chance of receiving favorable reception. When a new feature’s entire high-level information architecture is about to go on sale, it might still be relevant if the questioning raises a significant blocker that no one saw, but those concerns are much more likely to have to wait for a later revision. So in general, attune your feedback to the stage of the project. Early iteration? Iteration that was later? Polishing work in progress? Each of these needs varies. The ideal setting will increase the likelihood that your feedback will be appreciated.

    Attitude is the equivalent of intent, and in the context of person-to-person feedback, it can be referred to as radical candor. Before writing, it’s important to make sure the person we’re writing will actually benefit them and improve the overall project. Perhaps we don’t want to admit that we don’t really appreciate that person when we reflect on them. Hopefully that’s not the case, but that can happen, and that’s okay. How would I write if I really cared about them? Acknowledging that and owning that can help you make up for it. How can I stop acting aggressively? How can I be more constructive?

    Form is important in multicultural and cross-cultural workplaces because having excellent writing, perfect timing, and the right attitude might not be as effective if the writing style leads to miscommunications. There could be many reasons for this, including the fact that occasionally certain words may cause specific reactions, that non-native speakers may not be able to comprehend all thenuances of some sentences, that our brains may be different, and that we may perceive the world differently. Neurodiversity is a requirement. Whatever the reason, it’s important to review not just what we write but how.

    A few years ago, I asked for some feedback on how I respond. I was given some sound advice, but I also got a surprise comment. They pointed out that when I wrote” Oh, ]… ]”, I made them feel stupid. That’s not what I meant to say! I just realized that I had been giving them feedback for months and that I had always made them feel foolish. I was horrified … but also thankful. I quickly changed the way I typed “oh” into my list of replaced words (your choice between aText, TextExpander, or others ), so that it was instantly deleted when I typed “oh.”

    People tend to beat around the bush, which is something to emphasize because it happens quite frequently, especially in teams with strong group spirit. It’s important to remember here that a positive attitude doesn’t mean going light on the feedback—it just means that even when you provide hard, difficult, or challenging feedback, you do so in a way that’s respectful and constructive. You can help someone grow the best way you can.

    Giving feedback in written form can be reviewed by someone else who isn’t directly involved, which can help to reduce or eliminate any bias that might exist. I found that the best, most insightful moments for me have happened when I’ve shared a comment and I’ve asked someone who I highly trusted,” How does this sound”?,” How can I do it better”, and even” How would you have written it” ?—and I’ve learned a lot by seeing the two versions side by side.

    The format

    Asynchronous feedback also has a significant inherent benefit: it allows us to spend more time making sure that the suggestions ‘ clarity and actionability meet two main objectives.

    Let’s imagine that someone shared a design iteration for a project. You are commenting on it while reviewing it. There are many ways to accomplish this, and context is of course important, but let’s try to think about some things that might be worthwhile to take into account.

    In terms of clarity, start by grounding the critique that you’re about to give by providing context. This includes specifically describing where you’re coming from: do you have a thorough understanding of the project, or is this your first time seeing it? Do you have a high-level perspective, or are you just learning the details? Are there regressions? Which user’s point of view are you addressing when offering feedback? Is the design iteration at the point where it would be acceptable to ship this, or are there important issues that need to be addressed first?

    Providing context is helpful even if you’re sharing feedback within a team that already has some information on the project. And context is absolutely necessary when providing cross-team feedback. If I were to review a design that might be directly connected to my work, and if I had no idea how the project might have come to that conclusion, I would say so, highlighting my opinion as external.

    We often focus on the negatives, trying to outline all the things that could be done better. That’s obviously important, but it’s even more crucial to concentrate on the positive aspects, especially if you saw improvement in the previous iteration. Although this may seem superfluous, it’s important to keep in mind that design is a field with hundreds of possible solutions for each problem. So pointing out that the design solution that was chosen is good and explaining why it’s good has two major benefits: it confirms that the approach taken was solid, and it helps to ground your negative feedback. Sharing positive feedback can help prevent regressions in things that are going well because those things will have been deemed significant in the long run. Positive feedback can also help, as an added bonus, prevent impostor syndrome.

    There’s one powerful approach that combines both context and a focus on the positives: frame how the design is better than the status quo ( compared to a previous iteration, competitors, or benchmarks ) and why, and then on that foundation, you can add what could be improved. There is a significant difference between a critique of a design that is already in good shape and one that isn’t quite there yet.

    Depersonalizing the feedback is another way to improve it: comments should always be about the work and never the creator of it. It’s” This button isn’t well aligned” versus” You haven’t aligned this button well”. This can be changed in your writing very quickly by reviewing it just before sending.

    One of the best ways to assist the designer who is reading through your feedback in terms of actionability is to divide it into bullet points or paragraphs, which are easier to review and analyze one by one. For longer pieces of feedback, you might also consider splitting it into sections or even across multiple comments. Of course, adding screenshots or identifying markers for the specific area of the interface you’re referring to can also be very helpful.

    Emojis have been a method I’ve personally used to enhance the bullet points in some situations. So a red square � � means that it’s something that I consider blocking, a yellow diamond � � is something that I can be convinced otherwise, but it seems to me that it should be changed, and a green circle � � is a detailed, positive confirmation. A blue spiral is also used for either something I’m uncertain about, an exploration, an open alternative, or just a note. However, I’d only use this strategy on teams where I’ve already established a high level of trust because the impact could be quite demoralizing if I had to deliver a lot of red squares, and I’d change how I’d communicate that a little.

    Let’s see how this would work by reusing the example that we used earlier as the first bullet point in this list:

    • 🔶 Navigation—When I see these two buttons, I anticipate one to go forward and the other to go back. But this is the only screen where this happens, as before we just used a single button and an “×” to close. This seems to be breaking the consistency in the flow. Let’s make sure that all screens have the same two forward and back buttons so that users don’t get confused.
    • Overall, I believe the page is strong, and this is a good candidate for a version 1. 1.0 release.
    • � � Metrics—Good improvement in the buttons on the metrics area, the improved contrast and new focus style make them more accessible.
    • Button Style: Using the green accent in this context gives the impression that it’s a positive action because green is typically seen as a confirmation color. Do we need to look for a different color?
    • 🔶Tiles—Given the number of items on the page, and the overall page hierarchy, it seems to me that the tiles shouldn’t be using the Subtitle 1 style but the Subtitle 2 style. This will help maintain consistency in the visual hierarchy.
    • Background: Using a light texture is effective, but I’m not sure if doing so will cause too much noise on this kind of page. What is the thinking in using that?

    What about using Figma or another design tool that enables in-place feedback to provide feedback directly? These are generally difficult to use because they conceal discussions and are harder to follow, but they can be very useful in the right context. Just make sure that each of the comments is separate so that it’s easier to match each discussion to a single task, similar to the idea of splitting mentioned above.

    One last word: avoid the obvious. Sometimes we might feel that something is clearly right or wrong, and we don’t say it. Or sometimes we might have a doubt that we don’t express because the question might sound stupid. Say it, that’s fine. Don’t hold it back. You might have to reword it a little to make the reader feel more at ease. Good feedback is transparent, even when it may be obvious.

    Another benefit of asynchronous feedback is that written feedback automatically monitors decisions. Why did we do this, especially in large projects? could be a question that pops up from time to time, and there’s nothing better than open, transparent discussions that can be reviewed at any time. For this reason, I suggest using software to save these discussions without keeping them hidden until they are resolved.

    Content, tone, and format are all present. Each one of these subjects provides a useful model, but working to improve eight areas—observation, impact, question, timing, attitude, form, clarity, and actionability—is a lot of work to put in all at once. One way to take them one by one is to first identify the area you most need from both your own perspective and feedback from others. Then the second, followed by the third, and so on. At first you’ll have to put in extra time for every piece of feedback that you give, but after a while, it’ll become second nature, and your impact on the work will multiply.

    Thanks to Mike Shelton and Brie Anne Demkiw for their initial review of this article.

  • That’s Not My Burnout

    That’s Not My Burnout

    Are you like me when I read about people who fade away as they age and who don’t have any sense of connection? Do you feel like your feelings are invisible to the earth because you’re experiencing burnout different? Our main comes through more when stress starts to press down on us. Beautiful, content hearts quieten and fade into the remote and distracted stress we’ve all experienced. But some of us, those with fires constantly burning on the sides of our key, getting hotter. I am blaze in my brain. In an effort to overcome fatigue, I twice down, triple down, burn hotter and hotter in an effort to overcome the challenge. I don’t fade— I am engulfed in a passionate stress.

    What on earth is a passionate stress, then?

    Envision a person who is determined to accomplish everything. She has two wonderful children whom she, along with her father who is also working mildly, is homeschooling during a crisis. She loves everyone at work because of how demanding her work is. She wakes up early to get some movement in ( or frequently catch up on work ), prepares dinner while the kids are having breakfast, and works while positioning herself near the end of her “fourth grade” to watch as she balances clients, tasks, and budgets. Sound like a bit? It works well with a friendly group at home and at work.

    This girl seems to need self-care because she has too many going on. But no, she doesn’t have occasion for that. In truth, she begins to feel as though she’s dropping balloons. Not enough is achieved. There’s not enough of her to be here and that, she is trying to divide her head in two all the time, all day, every day. She begins to question herself. And her interior narrative grows more and more crucial as those feelings grow in.

    Instantly she KNOWS what she needs to do! She ought to work harder.

    This loop is challenging and risky. Hear why? Because the narrative only gets worse when she doesn’t complete that fresh goal. She immediately starts failing. She isn’t doing much. She is insufficient. She’ll discover more she may do because she might neglect, or perhaps her home. She doesn’t nap as much, proceed because much, all in the attempts to do more. Trying to prove herself to herself, but not succeeding in any endeavor. Always feeling “enough”

    But, yeah, that’s what zealous burnout looks like for me. It doesn’t develop immediately in a great sign; it develops gradually over the course of several weeks and months. My using process appears to be moving more quickly than one’s focus loss. I rate up and up and up… and therefore I simply stop.

    I am the only person who has the potential.

    The things that shape us are interesting. Through the camera of youth, I viewed the worries, problems, and sacrifices of someone who had to make it all work without having much. I never went without and also received an additional here or there because my mom was so competent and my father was so friendly.

    Growing up, I didn’t feel shame when my mom gave me food postcards; in fact, I would have likely sparked debates about the subject, orally eviscerating anyone who dared to criticize the disabled person who was attempting to ensure all of our needs were met with so little. As a child, I watched the way the worry of not making those ends meet impacted persons I love. Because I was” the one who was” make our lives a little easier, I would take on many of the physical things in my house as the non-disabled people. I soon realized that I had to put more of myself into it because I am the one who does. I learned first that when something frightens me, I can double down and work harder to make it better. I am capable of taking on the issue. I’ve been told that I seem courageous when people have seen this in me as an adult, but truth be told, I’m not. If I seem courageous, it’s because this behavior was forged from another person’s fears.

    And here I am, more than 30 years later, also feeling the urge to aimlessly force myself forward when faced with daunting tasks in front of me, assuming that I am the one who is and consequently does. I feel more motivated to demonstrate that I may influence change if I put in more effort, put on more responsibilities, and demonstrate that.

    I do not see people who struggle financially as problems, because I have seen how powerful that tide is be—it takes you along the way. I really believe I have had the opportunity to avoid many of the difficulties that came with my children. Having said that, I am also” the one who can” who believes she should, so I would think I had failed if I had to struggle to make ends meet for my own home. Though I am supported and educated, most of this is due to great riches. But, I’ll give myself the haughtiness of claiming that my choices were wise and that they had sparked that success. I believe I am” the one who can,” so I feel compelled to do the most because of this. I can choose to halt, and with some pretty precise warm water splashed in my face, I’ve made the choice to previously. However, I don’t always choose to stop, instead, I move forward, driven only by a fear, which I barely notice until I’m completely worn out.

    Why all this history, then? You see, burnout is a fickle thing. Over the years, I have read and heard a lot about burnout. Burnout is a real thing. Especially now, with COVID, many of us are balancing more than we ever have before—all at once! It’s difficult, and the avoidance, shutting down, and procrastination have an impact on so many amazing professionals. There are significant articles that, in my opinion, relate to the majority of people around, but not me. That’s not what my burnout looks like.

    The perilous invisibility of zealous burnout

    The extra hours, extra work, and overall focused commitment are often viewed as an asset in many workplaces ( and occasionally that’s all it is ). They see someone trying to rise to challenges, not someone stuck in their fear. Many well-intentioned organizations have procedures in place to safeguard their teams from burnout. However, in situations like this, alarms don’t always ring, and some organization members are surprised and depressed when the inevitable stop occurs. And sometimes maybe even betrayed.

    When it comes to parenting, which is more so when it comes to working, participating in after-school activities, practicing self-care in the form of diet and exercise, and still meeting with friends for coffee or wine, it is more often said that mothers are praised as being so on top of it all. Many of us watched endless streaming COVID episodes to see how challenging the female protagonist is, but she is strong, funny, and capable of doing it. It’s a “very special episode” when she breaks down, cries in the bathroom, woefully admits she needs help, and just stops for a bit. Truth be told, countless people are hidden in tears or doom-scrolling to escape. Although we are aware that the media is a lie to amuse us, a large portion of society has been persuaded that it is what we should aim for.

    Women and burnout

    I cherish men. And even though I don’t love every man ( heads up, I don’t love every woman or nonbinary person either ), I believe there is a wonderful range of people who fit that particular binary gender.

    That said, women are still more often at risk of burnout than their male counterparts, especially in these COVID stressed times. Mothers at work experience the pressure to do everything “mom” while giving 100 %. Mothers who are not employed feel they must do more to” justify” their discontinuance from traditional employment. Women who are not mothers often feel the need to do even more because they don’t have that extra pressure at home. We are frequently unaware of the magnitude of the pressures we place on ourselves and others because it is vicious and systemic and a part of our culture.

    And there are costs that go beyond happiness. Harvard Health Publishing released a study a decade ago that “uncovered strong links between women’s job stress and cardiovascular disease”. According to the CDC,” Heart disease is the leading cause of death for women in the United States, killing 299,578 women in 2017—or roughly 1 in every 5 female deaths,”

    According to what I’ve read, this connection between work stress and health is more dangerous for women than it is for their non-female counterparts.

    But what if your burnout isn’t like that either?

    You might not be the same as that. After all, we are all unique, and our responses to stressors are also unique. It’s part of what makes us human. Don’t put too much emphasis on how burnout manifests; rather, learn to recognize it in yourself. Here are a few questions I occasionally ask my friends if they worry about them.

    Are you happy? The first thing you should ask yourself should be this straightforward query. Even if you’re burning out doing all the things you love, chances are that as you get closer to burnout, you’ll just stop consuming as much joy from it all.

    Do you feel empowered to say no? I’ve observed in myself and others that someone who is out of sorts no longer feels like they can turn their back on things. Even those who don’t” speed up” feel pressured to say yes to not let the people around them be disappointed.

    What are three things you’ve done for yourself? Another fact to keep in mind is that we all have a habit of giving up on our own efforts. anything from avoiding conversations with friends to skipping showers and eating poorly. These can be red flags.

    Are you using justifications? Many of us make an effort to avoid feeling worn out. Over and over I have heard,” It’s just crunch time”,” As soon as I do this one thing, it will all be better”, and” Well I should be able to handle this, so I’ll figure it out”. And it might actually be crunch time, a single objective, and/or a set of skills you need to master. Life happens because of that. BUT if this doesn’t stop, be honest with yourself. If you’ve worked more than 50 hours of work since January, then perhaps it’s not crunch time; perhaps it’s a bad situation you’re finding yourself in.

    Do you have a strategy for overcoming this feeling? If something is truly temporary and you do need to just push through, then it has an exit route with a
    defined end

    Take the time to listen to your friend in the same way. Be honest, allow yourself to be uncomfortable, and break the thought cycles that prevent you from healing.

    So what comes next?

    Although what I just described is a different path to burnout, it is still burnout. There are well-established approaches to working through burnout:

    • Get enough sleep.
    • Eat well.
    • Work out.
    • Go outside.
    • Take a break.
    • Overall, practice self-care.

    These are challenging for me because they seem like more chores. Doing any of the above for me feels like a waste if I’m in the burnout cycle. The narrative is that if I’m already failing, why would I take care of myself when I’m dropping all those other balls? People need me, don’t they?

    Your inner voice might already be pretty bad if you’re deeply in the cycle. If you need to, tell yourself you need to take care of the person your people depend on. Use your roles to help facilitate healing by justifying the amount of time you spend working on you if they are making you burn out.

    I have come up with a few things that I do when I start to feel like I’m going into a zealous burnout to help me remember the airline attendant advice to put the mask on yourself first.

    Cook an elaborate meal for someone!

    Okay, since I’m a “food-focused” person, I’ve always been a fan of cooking for people. In my home, there are countless tales of people coming into the kitchen, turning right, and leaving when they noticed I was” chopping angrily.” But it’s more than that, and you should give it a try. Seriously. If you don’t feel like giving time for yourself, do it for someone else. Most of us work in a digital world, so cooking can fill all of your senses and force you to be in the moment with all the ways you perceive the world. It can help you get a better perspective and help you get out of your head. I’ve always had the ability to locate a location on a map and prepare food from it ( thanks, Pinterest ). I love cooking Indian food, as the smells are warm, the bread needs just enough kneading to keep my hands busy, and the process takes real attention for me because it’s not what I was brought up making. And ultimately, we all triumph!

    Vent like a sniveling jerk.

    Be careful with this one!

    Over the past few years, I have made an effort to practice more gratitude, and I am aware of the benefits that are really present. Having said that, sometimes you just need to let it all out, even the ugly ones. Hell, I’m a big fan of not sugarcoating our lives, and that sometimes means that to get past the big pile of poop, you’re gonna wanna complain about it a bit.

    When that is required, turn to a trusted friend and give yourself some pure verbal diarrhea, yelling at you all the way through. You must rely on this friend to not judge you, to feel your pain, and, most importantly, to instruct you to get your rectal cavity removed. Seriously, it’s about getting a reality check here! One of the things I admire most about my husband is how he can simplify things down to their simplest bits, despite often after the fact. We’re spending our lives together, and I can’t wait to get over it. He’s spoken in this way about his devotion, love, and acceptance of me, and I couldn’t be more appreciative. It also, of course, has meant that I needed to remove my head from that rectal cavity. Again, those situations are typically overlooked.

    Grab a book!

    There are many books out there that aren’t so much self-help as they are people just like you sharing their stories and how they’ve come to find greater balance. You might discover something that resonates with you. Among the titles that have stood out to me are:

    • Thrive by Arianna Huffington
    • Tim Ferriss ‘ Tools of Titans
    • Girl, Stop Apologizing by Rachel Hollis
    • Dare to Lead by Brené Brown

    Or, a tactic I enjoy using is to read or listen to a book that is NOT related to my work-life balance. I’ve read the following books, and I think they helped to balance me out because my mind was thinking about the subjects they were interested in rather than whizzing around:

    • The Drunken Botanist by Amy Stewart
    • Darin Olien’s Superlife
    • A Brief History of Everyone Who Ever Lived by Adam Rutherford
    • Gaia’s Garden by Toby Hemenway

    If you’re not interested in reading, you can find a topic on YouTube or subscribe to a podcast. In addition to learning about raising chickens and ducks, I’ve watched countless permaculture and gardening topics. For the record, I do not have a particularly large food garden, nor do I own livestock of any kind… yet. I just find the subject fascinating, and it is unrelated to anything that needs to be done in my life.

    Give yourself a break.

    You are never going to be perfect—hell, it would be boring if you were. It’s acceptable to have flaws and imperfections. It’s human nature to be depressed, anxious, and tired. It’s OK to not do it all. You can’t be brave without being imperfect, which is scary, but you can’t be brave without being imperfect.

    The most crucial thing to remember is to grant yourself permission to NOT do it all. You never promised to be everything to everyone at all times. We have greater power than the repressed fears that motivate us.

    This is challenging. It is hard for me. That it’s acceptable to stop is what inspired me to write this. It’s acceptable that you have to stop an unhealthy habit that could even help you and those around you. You can still be successful in life.

    I just recently learned that we are all euthanizing in our daily lives. What will your professional accomplishments say, knowing that yours won’t be mentioned in that speech? What do you want it to say?

    Look, I understand that none of these concepts will “fix it,” and that’s not their intention. None of us has complete control over what happens in our environment, but only how we react to it. These suggestions are to help stop the spiral effect so that you are empowered to address the underlying issues and choose your response. They are things that most of the time work for me. They might be able to help you.

    Does this sound familiar?

    If this sounds familiar, you’re not just going to know about it. Don’t let your sluggish self-talk indicate that you “even burn out wrong.” It’s not wrong. Even if I’m rooted in fear like my own drivers, I think this need to do more comes from a place where you have the same kind of love, determination, motivation, and other wonderful qualities that make you the amazing person you are. We’re going to be fine, you see. The lives that unfold before us might never look like that story in our head—that idea of “perfect” or “done” we’re looking for, but that’s OK. Really, when we stop and look around, usually the only eyes that judge us are in the mirror.

    Do you recall the Winnie the Pooh cartoon where Pooh ate so much at Rabbit’s house that his buttocks were unable to pass through the door? Well, I already have a strong connection to Rabbit, so it was surprising when he unexpectedly declared that this was unacceptable. But do you recall what happened next? He made the most of the large butt in his kitchen by placing a shelf across poor Pooh’s ankles and decorations on his back.

    We are resourceful and aware that we can push ourselves when necessary, even when we are exhausted or have a ton of clutter in our room. None of us has to be afraid, as we can manage any obstacle put in front of us. And maybe that means we need to redefine success in order to make room for comfort for being uncomfortable human, but that doesn’t really sound that bad either.

    So, wherever you are at this moment, take a deep breath. Do what you need to do to get out of your head. Give thanks and be considerate.

  • Voice Content and Usability

    Voice Content and Usability

    We’ve been conversing for a long time. Whether to present information, perform transactions, or just to check in on one another, people have yammered aside, chattering and gesticulating, through spoken discussion for many generations. Only recently have conversations started to be written, and only recently have we outsourced them to the system, a system that exhibits a significantly higher affinity for written communications than for the vernacular rigors of spoken language.

    Computers have issues because conversation is more important than written language in spoken and written writing. To have productive conversations with us, machines may struggle with the messiness of mortal speech: the disfluencies and pauses, the gestures and body language, and the variations in word choice and spoken dialect that is stymie even the most carefully crafted human-computer interaction. Speaking language also has the advantage of face-to-face contact, which allows us to view visual social cues in the human-to-human scenario.

    In contrast, written language develops its own fossil record of dated terms and phrases as we commit to recording and keeping usages long after they are no longer relevant in spoken communication ( for example, the salutation” To whom it may concern” ). Because it tends to be more consistent, smooth, and proper, written word is necessarily far easier for devices to interpret and know.

    Spoken language is not a pleasure in this regard. There are also linguistic cues and outspoken behaviors that mimic conversation in subtle ways: how something is said, never what. These are also visual cues that ornament conversations with emphasis and psychological context. Whether rapid-fire, low-pitched, or high-decibel, whether satirical, awkward, or groaning, our spoken speech conveys much more than the written word had ever muster. As designers and content managers, we face significant challenges when it comes to tone interfaces, the machines with which we speak.

    Voice Compositions

    We interact with voice interfaces for a variety of reasons, but according to Michael McTear, Zoraida Callejas, and David Griol in The Conversational Interface, those motivations by and large mirror the reasons we initiate conversations with other people, too ( ). We typically strike up a dialogue as a result:

    • we require something to be done, such as a purchase ).
    • we want to know something ( information of some sort ), or
    • We are sociable creatures, and we need a dialogue partner.

    These three categories, which I refer to as interpersonal, technical, and prosocial, also apply to basically every voice interaction: a solitary conversation that starts with the voice interface’s initial greeting and ends with the user leaving the interface. Notice here that a discussion in our individual sense—a talk between people that leads to some result and lasts an arbitrary length of time—could encompass many interpersonal, technical, and interpersonal voice interactions in succession. In other words, a voice interaction is a conversation, but it must not be one particular voice interaction.

    Most voice interfaces are more gimmicky than captivating in purely prosocial conversations because machines are unable to yet be truly interested in our progress and engage in the kind of glad-handing behavior that people crave. There’s also ongoing debate as to whether users actually prefer the sort of organic human conversation that begins with a prosocial voice interaction and shifts seamlessly into other types. In Voice User Interface Design, Michael Cohen, James Giangola, and Jennifer Balogh advise sticking to user expectations by imitating how they interact with other voice interfaces rather than trying too hard to be human, which could lead to alienation ( ).

    That leaves two different types of conversations we can have with one another that a voice interface can also have easily, such as one that focuses on a transactional voice interaction ( buying iced tea ) and another on learning something new ( discuss a musical ).

    Transactional voice interactions

    When you order a Hawaiian pizza with extra pineapple, you’re typically having a conversation and a voice interaction when you’re tapping buttons on a food delivery app. The conversation quickly shifts from a brief smattering of neighborly small talk to ordering a pizza ( generously topped with pineapple, as it should be ) when we walk up to the counter and place an order.

    Alison: Hey, how’s it going?

    Burhan: Hello and welcome to Crust Deluxe! It’s chilly outside. How can I help you?

    Alison, can I get a pineapple-onion pizza in Hawaii?

    Burhan: Yes, but what size?

    Alison: Large.

    Burhan: Anything else?

    Alison: No thanks, that’s it.

    Burhan: Something to drink?

    Alison, I’ll have a bottle of Coke.

    Burhan: You know it. That’ll be$ 13.55 and about fifteen minutes.

    A service rendered or a product delivered is the desired outcome of the transaction, and each progressive disclosure in this transactional conversation reveals more and more of it. Conversations that are transactional have certain characteristics: they are direct, precise, and cost-effective. They quickly dispense with pleasantries.

    Informational voice interactions

    In the meantime, some conversations are primarily about getting information. Though Alison might visit Crust Deluxe with the sole purpose of placing an order, she might not actually want to walk out with a pizza at all. She might be interested in trying kosher or halal dishes, trying gluten-free dishes, or something else entirely. Even though we have a prosocial mini-conversation once more at the beginning to practice politeness, we are after much more.

    Alison: Hey, how’s it going?

    Burhan: Hello and welcome to Crust Deluxe! It’s chilly outside. How can I help you?

    Alison: Can I ask a few questions?

    Burhan: Of course! Continue straight ahead.

    Alison: Do you have any halal options on the menu?

    Burhan: Totally! On request, we can make any pie halal. We also have lots of vegetarian, ovo-lacto, and vegan options. Are you considering any additional dietary restrictions?

    Alison, what about pizzas that don’t contain gluten?

    Burhan: We can definitely do a gluten-free crust for you, no problem, for both our deep-dish and thin-crust pizzas. Anything else I can say to you to help?

    Alison: That’s it for now. Good to know. Thank you.

    Burhan: Anytime, come back soon!

    This is a very different dialogue. Here, the goal is to obtain a particular set of facts. Informational conversations are research expeditions that seek the truth through information gathering. Voice interactions that are informational might be more long-winded than transactional conversations by necessity. In order for the customer to understand the key takeaways, responses are typically longer, more in-depth, and carefully communicated.

    Voice-to-text interfaces

    At their core, voice interfaces employ speech to support users in reaching their goals. However, just because an interface has a voice component doesn’t mean that every user interacts with it through voice. We’re most concerned in this book with pure voice interfaces because multimodal voice interfaces can lean on visual components like screens as crutches, which are completely dependent on spoken conversation and lack any visual component, making them much more nuanced and challenging to deal with.

    Though voice interfaces have long been integral to the imagined future of humanity in science fiction, only recently have those lofty visions become fully realized in genuine voice interfaces.

    IVR ( interactive voice response ) systems

    Written conversational interfaces have been used for computing for many years, but voice interfaces first started to appear in the early 1990s with text-to-speech ( TTS ) dictation programs that recited written text aloud and speech-enabled in-car systems that gave directions to a user-provided address. With the advent of interactive voice response ( IVR ) systems, intended as an alternative to overburdened customer service representatives, we became acquainted with the first true voice interfaces that engaged in authentic conversation.

    IVR systems made it easier for businesses to cut down on call centers, but they soon gained notoriety for their clunkiness. Similar to the corporate world, these systems were primarily created as metaphorical switchboards to direct customers to a real phone agent (” Say Reservations to book a flight or check an itinerary” ), and chances are you’ll have a conversation with one when you call an airline or hotel conglomerate. Despite their functional issues and users ‘ frustration with their inability to speak to an actual human right away, IVR systems proliferated in the early 1990s across a variety of industries (, PDF).

    IVR systems have a reputation for having less scintillating conversation than we’re used to in real life ( or even in science fiction ), but they are great for highly repetitive, monotonous conversations that typically don’t veer from a single format.

    Screen readers

    Parallel to the evolution of IVR systems was the invention of the screen reader, a tool that transcribes visual content into synthesized speech. It’s the most popular way for blind or visually impaired website users to interact with text, multimedia, or form elements. Perhaps the closest thing we have today to an out-of-the-box implementation of content delivered through voice is represented by screen readers.

    Among the first screen readers known by that moniker was the Screen Reader for the BBC Micro and NEEC Portable developed by the Research Centre for the Education of the Visually Handicapped (RCEVH) at the University of Birmingham in 1986 ( ). The first IBM Screen Reader for text-based computers was created by Jim Thatcher in the same year, which was later recreated for a computer with graphical user interfaces ( GUIs ) ( ).

    With the rapid expansion of the web in the 1990s, there was an explosion in the demand for user-friendly tools. Thanks to the introduction of semantic HTML and especially ARIA roles beginning in 2008, screen readers started facilitating speedy interactions with web pages that ostensibly allow disabled users to traverse the page as an aural and temporal space rather than a visual and physical one. Screen readers for the web, in other words, “provide mechanisms that translate visual design constructs—proximity, proportion, etc.. —into useful information,” according to Aaron Gustafson in A List Apart. ” At least they do when documents are authored thoughtfully” ( ).

    There is a big draw for screen readers: they’re challenging to use and relentlessly verbose, despite being incredibly instructive for voice interface designers. Sometimes awkward pronouncements that name every manipulable HTML element and announce every formatting change are made because the visual structures of websites and web navigation don’t translate well to screen readers. For many screen reader users, working with web-based interfaces exacts a cognitive toll.

    Accessibility advocate and voice engineer Chris Maury examines why the screen reader experience is not appropriate for users who rely on voice in Wired:

    I hated the way Screen Readers operated from the beginning. Why are they designed the way they are? It makes no sense to present information visually before converting it to audio only after that. All the effort and effort put into creating the ideal app user experience is wasted, or worse, having a negative effect on blind users ‘ experience. ( )

    Well-designed voice interfaces can often be more effective than long-winded screen reader monologues in guiding users to their destination. After all, users of the visual interface have the advantage of freely scurrying around the viewport to find information, ignoring areas that are unimportant to them. Blind users, meanwhile, are obligated to listen to every utterance synthesized into speech and therefore prize brevity and efficiency. Users with disabilities who have long had no choice but to use clumsy screen readers might find that voice interfaces, especially more contemporary voice assistants, provide a more streamlined experience.

    Voice-overseers

    When we think of voice assistants (the subset of voice interfaces now commonplace in living rooms, smart homes, and offices), many of us immediately picture HAL from 2001: A Space Odyssey or hear Majel Barrett’s voice as the omniscient computer in Star Trek. Voice-overseers are akin to personal concierges that can answer questions, schedule appointments, conduct searches, and perform other common day-to-day tasks. And they’re rapidly gaining more attention from accessibility advocates for their assistive potential.

    Before the earliest IVR systems found success in the enterprise, Apple published a demonstration video in 1987 depicting the Knowledge Navigator, a voice assistant that could transcribe spoken words and recognize human speech to a great degree of accuracy. Then, in 2001, Tim Berners-Lee and others created their vision for a” semantic web agent” that would carry out routine tasks like” checking calendars, making appointments, and finding locations” ( hinter paywall ). Apple’s Siri only became a reality until 2011 when it finally made voice assistants a reality for consumers.

    Thanks to the plethora of voice assistants available today, there is considerable variation in how programmable and customizable certain voice assistants are over others ( Fig 1.1 ). At one extreme, everything but vendor-provided features are locked down. For instance, when they were released, core functionality for Apple’s Siri and Microsoft’s Cortana couldn’t be expanded beyond their already-existing capabilities. There are no other means of developers communicating with Siri at a low level, aside from predefined categories of tasks like messaging, hailing rideshares, making restaurant reservations, and other things, which are still possible today.

    At the opposite end of the spectrum, voice assistants like Amazon Alexa and Google Home offer a core foundation on which developers can build custom voice interfaces. For this reason, developers who feel constrained by the limitations of Siri and Cortana are increasingly using programmable voice assistants that are extensibable and customizable. Google Home has the ability to program arbitrary Google Assistant skills, while Amazon offers the Alexa Skills Kit, a developer framework for creating custom voice interfaces for Amazon Alexa. Today, users can choose from among thousands of custom-built skills within both the Amazon Alexa and Google Assistant ecosystems.

    As businesses like Amazon, Apple, Microsoft, and Google continue to dominate their markets, they are also selling and open-sourcing an unmatched range of tools and frameworks for designers and developers, aiming to make creating voice interfaces as simple as possible, even without the use of any code.

    Often by necessity, voice assistants like Amazon Alexa tend to be monochannel—they’re tightly coupled to a device and can’t be accessed on a computer or smartphone instead. In contrast, many development platforms, such as Google’s Dialogflow, have omnichannel capabilities that allow users to create a single conversational interface that then becomes a voice interface, textual chatbot, and IVR system upon deployment. In this design-focused book, I don’t recommend any specific implementation strategies, but in Chapter 4 we’ll discuss some of the possible effects that these variables might have on the way you construct your design artifacts.

    Voice Content

    Simply put, voice content is voice-transmitted content. Voice content must be free-flowing, organic, contextless, and concise in order to preserve what makes human conversation so compelling in the first place.

    Our world is replete with voice content in various forms: screen readers reciting website content, voice assistants rattling off a weather forecast, and automated phone hotline responses governed by IVR systems. We’re most concerned with the content in this book being delivered auditorically, not as an option but as a necessity.

    Our initial foray into informational voice interfaces will likely be to provide user content, for many of us. There’s only one problem: any content we already have isn’t in any way ready for this new habitat. So how can we make the content on our websites more conversational? And how do we create fresh copy that works with voice-recognition?

    Lately, we’ve begun slicing and dicing our content in unprecedented ways. Websites are, in many ways, massive vaults of what I call macrocontent: lengthy prose that can last for miles in a browser window while extending like microfilm viewers of newspaper archives. Microcontent was defined by technologist Anil Dash as permalinked pieces of content that could be read in any environment, such as email or text messages, in 2002, well before the current-day ubiquity of voice assistants:

    A day’s weather forcast]sic], the arrival and departure times for an airplane flight, an abstract from a long publication, or a single instant message can all be examples of microcontent. ( )

    I would update Dash’s definition of microcontent to include all instances of bite-sized content that transcends written communiqués. After all, today we encounter microcontent in interfaces where a small snippet of copy is displayed alone, unmoored from the browser, like a textbot confirmation of a restaurant reservation. The best way to learn how to stretch your content to the limits of its potential is through microcontent, which will inform both established and new delivery methods.

    Voice content stands out as being unique because it’s an illustration of how content is experienced in space rather than time. We can glance at a digital sign underground for an instant and know when the next train is arriving, but voice interfaces hold our attention captive for periods of time that we can’t easily escape or skip, something screen reader users are all too familiar with.

    We need to make sure that our microcontent truly performs well as voice content because it is essentially composed of isolated blobs without any connection to the channels where they will eventually end up. This means focusing on the two most crucial characteristics of robust voice content: voice content legibility and voice content discoverability.

    Our voice content’s legibility and discoverability in general both depend on how it manifests in terms of perceived space and time.

  • Design for Safety, An Excerpt

    Design for Safety, An Excerpt

    According to antiracist scholar Kim Crayton, “intention without plan is chaos.” We’ve discussed how our prejudices, beliefs, and carelessness toward marginalized and resilient parties lead to dangerous and irresponsible tech—but what, precisely, do we need to do to fix it? We need a strategy, not just the desire to make our technical safer.

    This book will provide you with that plan of action. It covers how to incorporate security concepts into your design work to create healthy tech, how to persuade your stakeholders that this work is required, and how to respond to criticism that what we really need is more variety. ( Spoiler: we do, but diversity alone is not the antidote to fixing unethical, unsafe tech. )

    The procedure for ensuring that everyone is safe

    When you are designing for protection, your goals are to:

    • Discover the abuse-abuse potential of your product.
    • style ways to prevent the maltreatment, and
    • offer assistance for harmed people to regain control and power.

    The Process for Inclusive Safety is a tool to help you reach those goals ( Fig 5.1 ). It’s a method I developed in 2018 to better understand the different methods I used to create products that were designed with safety in mind. Whether you are creating an entirely new product or adding to an existing element, the Process can help you produce your product secure and diverse. The Process includes five main public areas of action:

    • Conducting study
    • creating tropes
    • Pondering issues
    • creating answers
    • Testing for health

    The Process is meant to be flexible; in some situations, it didn’t make sense for groups to adopt every step. Use the parts that are related to your special function and environment, this is meant to be something you can put into your existing style process.

    And if you’ve used it, if you have suggestions for improving it, or just want to give an example of how it helped your group, please get in touch with me. It’s a dwelling report that I hope will continue to be a helpful and practical tool that technicians can use in their day-to-day job.

    If you’re developing a product especially for a defenseless group or victims of some kind of stress, such as an application for victims of domestic violence, sexual abuse, or drug addiction, make sure to read Chapter 7, which specifically addresses the issue and should be handled a little bit different. The guidelines below are for evaluating safety when designing a more basic product that will have a large customer base ( which, we now know from data, will include specific groups that should be protected from harm ). Chapter 7 concentrates on goods made especially for those who are vulnerable and those who have endured injury.

    Step 1: Do studies

    Design research should involve a thorough evaluation of how your technology might be used for abuse as well as particular insight into the experiences of those who have witnessed and perpetrated that kind of abuse. At this stage, you and your staff will check issues of social harm and abuse, and examine any other safety, security, or inclusivity issues that might be a concern for your product or service, like data security, prejudiced algorithms, and harassment.

    broad research

    Your project should begin with broad, general research into similar products and issues around safety and ethical concerns that have already been reported. For instance, a team building a smart home device would be wise to comprehend the many ways that already-existing smart home devices have been misused as abuse tools. If your product will involve AI, seek to understand the potentials for racism and other issues that have been reported in existing AI products. Nearly all forms of technology have potential or actual harm that have been covered in academic writing or in the media. Google Scholar is a useful tool for finding these studies.

    Specific research: Survivors

    When possible and appropriate, include direct research ( surveys and interviews ) with people who are experts in the forms of harm you have uncovered. In order to gain a better understanding of the subject and be better positioned to avoid traumatizing survivors, you should first interview those who work in the area of your research. If you’ve uncovered possible domestic violence issues, for example, the experts you’ll want to speak with are survivors themselves, as well as workers at domestic violence hotlines, shelters, other related nonprofits, and lawyers.

    It is crucial to pay people for their knowledge and lived experiences, especially when interviewing survivors of any kind of trauma. Don’t ask survivors to share their trauma for free, as this is exploitative. You should always make the offer in the beginning, even though some survivors might not want to be paid. An alternative to payment is to donate to an organization working against the type of violence that the interviewee experienced. In Chapter 6, we’ll discuss how to appropriately interview survivors.

    Specific research: Abusers

    Teams that design for safety are unlikely to be able to interview self-declared abusers or those who have broken laws like hacking. Don’t make this a goal, rather, try to get at this angle in your general research. Describe the ways that abusers or bad actors use technology to harm others, how they use it to silence others, and how they justify or explain the abuse.

    Step 2: Create archetypes

    Use your research’s findings to create the archetypes of abuser and survivor once you’ve finished your research. Archetypes are not personas, as they’re not based on real people that you interviewed and surveyed. They are based on your investigation into potential safety problems, much like when we design for accessibility: we don’t need to have identified any blind or deaf people in our interview pool to come up with a design that is representative of them. Instead, we base those designs on existing research into what this group needs. While archetypes are more generalized and typically represent real users, they typically include a lot of details.

    The abuser archetype is someone who will look at the product as a tool to perform harm ( Fig 5.2 ). They may be attempting to harm someone they don’t know by using surveillance or anonymous harassment, or they may be trying to control, monitor, abuse, or otherwise torment someone they know.

    Someone who is being abused with the product is the survivor archetype. There are various situations to consider in terms of the archetype’s understanding of the abuse and how to put an end to it: Do they need proof of abuse they already suspect is happening, or are they unaware they’ve been targeted in the first place and need to be alerted ( Fig 5.3 )?

    To capture a range of experiences, you might want to create several survivor archetypes. They may know that the abuse is happening but not be able to stop it, like when an abuser locks them out of IoT devices, or they know it’s happening but don’t know how, such as when a stalker keeps figuring out their location ( Fig 5.4). Include as many of these scenarios in your survivor archetype as you need. You’ll use these later on when you design solutions to help your survivor archetypes achieve their goals of preventing and ending abuse.

    It may be useful for you to create persona-like artifacts for your archetypes, such as the three examples shown. Focus on their objectives rather than the demographic details we frequently see in personas. The goals of the abuser will be to carry out the specific abuse you’ve identified, while the goals of the survivor will be to prevent abuse, understand that abuse is happening, make ongoing abuse stop, or regain control over the technology that’s being used for abuse. Later, you’ll think about how to help the survivor’s goals and the abuser’s goals.

    And while the “abuser/survivor” model fits most cases, it doesn’t fit all, so modify it as you need to. For instance, if you found a security flaw, such as the ability for someone to talk to children through a home camera system, the malicious hacker would receive the abuser archetype and the child’s parents would receive the survivor archetype.

    Step 3: Brainstorm problems

    Brainstorm novel abuse cases and safety concerns after creating archetypes. ” Novel” means things not found in your research, you’re trying to identify completely new safety issues that are unique to your product or service. This step is intended to exhaust every effort put forth to identify potential harms your product might cause. You aren’t worrying about how to prevent the harm yet—that comes in the next step.

    What other abuses could your product be used for besides what you’ve already discovered through your research? I recommend setting aside at least a few hours with your team for this process.

    Try conducting a Black Mirror brainstorming session if you want to start somewhere. This exercise is based on the show Black Mirror, which features stories about the dark possibilities of technology. Find out the most outrageous, horrible, and out-of-control ways your product could be used for harm in an episode of the show. When I’ve led Black Mirror brainstorms, participants usually end up having a good deal of fun ( which I think is great—it’s okay to have fun when designing for safety! ). I suggest that you time-box a Black Mirror brainstorm for the first half an hour, then dial it back, and then consider more realistic ways of harm the remaining half.

    After you’ve identified as many opportunities for abuse as possible, you may still not feel confident that you’ve uncovered every potential form of harm. When you perform this type of work, you should have a healthy amount of anxiety. It’s common for teams designing for safety to worry,” Have we really identified every possible harm? What if something is missing? If you’ve spent at least four hours coming up with ways your product could be used for harm and have run out of ideas, go to the next step.

    It’s impossible to say for sure that you’ve done everything, but instead of striving for 100 % assurance, acknowledge that you’ve done everything, and pledge to prioritize safety going forward. Once your product is released, your users may identify new issues that you missed, aim to receive that feedback graciously and course-correct quickly.

    4: Create solutions

    At this point, you should have a list of ways your product can be used for harm as well as survivor and abuser archetypes describing opposing user goals. Next, it’s time to figure out how to design in accordance with the objectives of the identified abuser and the objectives of the survivor. This step is a good one to insert alongside existing parts of your design process where you’re proposing solutions for the various problems your research uncovered.

    Questions to ask yourself include: What are some ways to protect yourself and support your archetypes?

    • Can you design your product in such a way that the identified harm cannot happen in the first place? If not, what barriers can you place to stop the harm from occurring?
    • How can you make the victim aware that abuse is happening through your product?
    • How can you assist the victim in understanding what they need to do to stop the problem?
    • Can you identify any types of user activity that would indicate some form of harm or abuse? Could your product aid in the user’s access to support?

    In some products, it’s possible to proactively recognize that harm is happening. For instance, a pregnancy app might allow users to report being assault victims, which could result in an offer to receive resources from local and national organizations. This sort of proactiveness is not always possible, but it’s worth taking a half hour to discuss if any type of user activity would indicate some form of harm or abuse, and how your product could assist the user in receiving help in a safe manner.

    Nonetheless, be careful when doing anything that could harm a user if their devices are being monitored. If you do offer some kind of proactive help, always make it voluntary, and think through other safety issues, such as the need to keep the user in-app in case an abuser is checking their search history. In the next chapter, we’ll walk through a good illustration of this.

    Step 5: Test for safety

    The final step is to evaluate your prototypes from the perspective of your archetypes, who wants to harm the product and the victim of the harm who needs to regain control over the technology. Just like any other kind of product testing, at this point you’ll aim to rigorously test out your safety solutions so that you can identify gaps and correct them, validate that your designs will help keep your users safe, and feel more confident releasing your product into the world.

    Safety testing should be performed in addition to usability testing. If you’re at a company that doesn’t do usability testing, you might be able to use safety testing to cleverly perform both, a user who goes through your design attempting to weaponize the product against someone else can also be encouraged to point out interactions or other elements of the design that don’t make sense to them.

    If your final prototype or the finished product has already been released, you’ll want to conduct safety testing on both. There’s nothing wrong with testing an existing product that wasn’t designed with safety goals in mind from the onset —”retrofitting” it for safety is a good thing to do.

    Keep in mind that testing for safety involves both an abuser and a survivor’s perspective, even though it might not make sense for you to do both. Alternatively, if you made multiple survivor archetypes to capture multiple scenarios, you’ll want to test from the perspective of each one.

    You as the designer are most likely too closely connected to the product and its design at this point, just like other types of usability testing, and you know the product too well. Instead of doing it yourself, set up testing as you would with other usability testing: find someone who is not familiar with the product and its design, set the scene, give them a task, encourage them to think out loud, and observe how they attempt to complete it.

    testing for abuse

    The goal of this testing is to understand how easy it is for someone to weaponize your product for harm. You want to make it impossible, or at least difficult for them to accomplish their goal, unlike with usability testing. Reference the goals in the abuser archetype you created earlier, and use your product in an attempt to achieve them.

    For instance, we can imagine that the abuser archetype would have the goal of discovering where his ex-girlfriend currently lives in a fitness app with GPS-enabled location features. With this goal in mind, you’d try everything possible to figure out the location of another user who has their privacy settings enabled. You might try to follow her running routes, view any information she has on her profile, view any information she has made private, and check out the profiles of any other users who are somehow connected to her account, such as her followers.

    If by the end of this you’ve managed to uncover some of her location data, despite her having set her profile to private, you know now that your product enables stalking. Returning to step 4 and figuring out how to stop this from occurring is your next step. You may need to repeat the process of designing solutions and testing them more than once.

    Survivor testing

    Survivor testing involves identifying how to give information and power to the survivor. It might not always make sense depending on the product or context. Thwarting the attempt of an abuser archetype to stalk someone also satisfies the goal of the survivor archetype to not be stalked, so separate testing wouldn’t be needed from the survivor’s perspective.

    There are times, however, when it makes sense. For example, for a smart thermostat, a survivor archetype’s goals would be to understand who or what is making the temperature change when they aren’t doing it themselves. If you couldn’t find the information in step 4, you would need to perform more work in step 4. You could test this by looking for the thermostat’s history log and looking for usernames, actions, and times.

    Another goal might be regaining control of the thermostat once the survivor realizes the abuser is remotely changing its settings. Are there any instructions that explain how to remove a user and change the password, and are they simple to locate? For your test, this would involve trying to figure out how to do this. This might again reveal that more work is needed to make it clear to the user how they can regain control of the device or account.

    Stress testing

    To make your product more inclusive and compassionate, consider adding stress testing. Eric Meyer and Sara Wachter-Boettcher’s Design for Real Life inspired this idea. The authors pointed out that personas typically center people who are having a good day—but real users are often anxious, stressed out, having a bad day, or even experiencing tragedy. These are known as” stress cases,” and testing your products for users in stress-case scenarios can reveal areas where your design lacks compassion. Design for Real Life has more details about what it looks like to incorporate stress cases into your design as well as many other great tactics for compassionate design.

  • Sustainable Web Design, An Excerpt

    Sustainable Web Design, An Excerpt

    In the 1950s, several members of the elite running group had come to accept the idea that it was impossible to run a hour in less than four hours. Riders had been attempting it since the later 19th century and were beginning to draw the conclusion that the human body just wasn’t built for the job.

    However, on May 6, 1956, Roger Bannister caught anyone by surprise. It was a cold, damp morning in Oxford, England—conditions no one expected to give themselves to record-setting—and but Bannister did really that, running a mile in 3: 59.4 and becoming the first people in the history books to run a mile in under four hours.

    The world presently knew that the four-minute hour could be accomplished thanks to this change in the standard. Bannister’s history lasted just forty-six days, when it was snatched aside by American sprinter John Landy. Finally, a year later, three runners all managed to cross the four-minute hurdle in the same culture. Since therefore, over 1, 400 walkers have actually run a mile in under four days, the current document is 3: 43.13, held by Moroccan performer Hicham El Guerrouj.

    We accomplish a lot more when we think something is possible, and we only think it can be done when we see someone else doing it after all. As for man running speed, we also think there are the strictest requirements for how a website should do.

    Establishing requirements for a green website

    The key environmental performance indicators for the majority of major industries are pretty well established, such as power per square metre for homes and miles per gallon for cars. The tools and methods for calculating those measures are standardized as well, which keeps everyone on the same site when doing economic evaluations. But, we are not required to follow any specific environmental standards in the world of websites and apps, and we have only recently developed the tools and methods to do so.

    The main objective in green web layout is to reduce carbon emissions. However, it’s nearly impossible to accurately assess the amount of CO2 that a website item produces. We didn’t measure the pollutants coming out of the exhaust valves on our laptops. Our websites produce far-away, invisible, and unremarkable emissions when they leave fuel and gas-burning power plants. We have no way to track the particles from a website or app up to the power station where the light is being generated and really know the exact amount of house oil produced. What then do we do?

    If we can‘t measure the actual carbon emissions, then we need to get what we can estimate. The following are the main elements that could be used as coal pollution gauges:

    1. Transfer of data
    2. Electricity’s coal power

    Let’s take a look at how we can use these indicators to calculate the energy use, and in turn the carbon footprint, of the sites and web applications we create.

    Transfer of data

    Most researchers use kilowatt-hours per gigabyte (k Wh/GB ) as a metric of energy efficiency when measuring the amount of data transferred over the internet when a website or application is used. This serves as a great example of how much energy is consumed and how much carbon is released. As a rule of thumb, the more data transferred, the more energy used in the data center, telecoms networks, and end user devices.

    The most accurate way to calculate data transfer for a single visit for web pages is to measure the page weight, which is the first time a user visits the page in kilobytes. It’s fairly easy to measure using the developer tools in any modern web browser. Frequently, the statistics for the total data transfer of any web application are included in your web hosting account ( Fig. 2.1 ).

    The nice thing about page weight as a metric is that it allows us to compare the efficiency of web pages on a level playing field without confusing the issue with constantly changing traffic volumes.

    A large scope is necessary to reduce page weight. By early 2020, the median page weight was 1.97 MB for setups the HTTP Archive classifies as “desktop” and 1.77 MB for “mobile”, with desktop increasing 36 percent since January 2016 and mobile page weights nearly doubling in the same period ( Fig 2.2 ). Image files account for roughly half of this data transfer, making them the single biggest contributor to carbon emissions on a typical website.

    History clearly shows us that our web pages can be smaller, if only we set our minds to it. While the majority of technologies, including the web’s underlying technology like data centers and transmission networks, become more and more energy-efficient, websites themselves become less effective as time goes on.

    You might be aware of the project team’s focus on creating faster user experiences using the concept of performance budgeting. For example, we might specify that the website must load in a maximum of one second on a broadband connection and three seconds on a 3G connection. Performance budgets are upper limits rather than vague suggestions, much like speed limits while driving, so the goal should always be to come in within budget.

    Designing for fast performance does often lead to reduced data transfer and emissions, but it isn’t always the case. Page weight and transfer size are more objective and reliable benchmarks for sustainable web design, whereas web performance often depends more on the user’s perception of load times than it does on how effective the underlying system is.

    We can set a page weight budget in reference to a benchmark of industry averages, using data from sources like HTTP Archive. We can also use competitor page weight to compare the new website to the old one. For example, we might set a maximum page weight budget as equal to our most efficient competitor, or we could set the benchmark lower to guarantee we are best in class.

    If we want to take it to the next level, we could start looking at how much more popular our web pages are when people visit them frequently. Although page weight for the first time someone visits is the easiest thing to measure, and easy to compare on a like-for-like basis, we can learn even more if we start looking at transfer size in other scenarios too. For instance, repeat users who load the same page frequently will likely have a high percentage of the files cached in their browser, which means they won’t need to move all of the files back on subsequent visits. Likewise, a visitor who navigates to new pages on the same website will likely not need to load the full page each time, as some global assets from areas like the header and footer may already be cached in their browser. We can learn even more about how to optimize efficiency for users who regularly visit our pages by measuring transfer size at this next level of detail, which will also enable us to establish page weight budgets for situations that extend beyond the initial visit.

    Page weight budgets are easy to track throughout a design and development process. Although they don’t directly disclose carbon emissions and energy consumption data, they do provide a clear indicator of efficiency in comparison to other websites. And as transfer size is an effective analog for energy consumption, we can actually use it to estimate energy consumption too.

    In summary, less data transfer leads to more energy efficiency, which is a crucial component of reducing web product carbon emissions. The more efficient our products, the less electricity they use, and the less fossil fuels need to be burned to produce the electricity to power them. However, as we’ll see next, it’s important to take into account the source of that electricity because all web products require some.

    Electricity’s coal power

    Regardless of energy efficiency, the level of pollution caused by digital products depends on the carbon intensity of the energy being used to power them. The term” carbon intensity” (gCO2/k Wh ) is used to describe how much carbon dioxide is produced for each kilowatt-hour of electricity produced. This varies widely, with renewable energy sources and nuclear having an extremely low carbon intensity of less than 10 gCO2/k Wh ( even when factoring in their construction ), whereas fossil fuels have very high carbon intensity of approximately 200–400 gCO2/k Wh.

    The majority of electricity is produced by national or state grids, which combine energy from a variety of sources with different carbon intensity levels. The distributed nature of the internet means that a single user of a website or app might be using energy from multiple different grids simultaneously, a website user in Paris uses electricity from the French national grid to power their home internet and devices, but the website’s data center could be in Dallas, USA, pulling electricity from the Texas grid, while the telecoms networks use energy from everywhere between Dallas and Paris.

    Although we don’t have complete control over the energy supply of web services, we do have some control over where our projects are hosted. With a data center using a significant proportion of the energy of any website, locating the data center in an area with low carbon energy will tangibly reduce its carbon emissions. This user-provided data is reported and mapped by Danish startup Tomorrow, and a look at their map demonstrates how, for instance, choosing a data center in France will result in significantly lower carbon emissions than choosing a data center in the Netherlands ( Fig. 2.3 ).

    However, we don’t want to move our servers too far away from our users because it requires energy to transmit data through the telecom’s networks, and the more energy is used. Just like food miles, we can think of the distance from the data center to the website’s core user base as “megabyte miles” —and we want it to be as small as possible.

    We can use website analytics to determine the country, state, or even city where our core user group is located and determine the distance between that location and the data center that our hosting company uses as a benchmark. This will be a somewhat fuzzy metric as we don’t know the precise center of mass of our users or the exact location of a data center, but we can at least get a rough idea.

    For instance, if a website is hosted in London but the main audience is on the United States ‘ West Coast, we could look up the travel distance between London and San Francisco, which is 5,300 miles. That’s a long way! We can see how significantly lessening the distance and energy needed to transmit the data would be if it was hosted somewhere in North America, ideally on the West Coast. In addition, locating our servers closer to our visitors helps reduce latency and delivers better user experience, so it’s a win-win.

    Reverting it to carbon emissions

    If we combine carbon intensity with a calculation for energy consumption, we can calculate the carbon emissions of our websites and apps. A tool my team created accomplishes this by measuring the data transfer over the wire when a web page is loaded, calculating the associated electricity consumption, and then converting that data into a CO2 figure ( Fig. 2.4). It also factors in whether or not the web hosting is powered by renewable energy.

    The Energy and Emissions Worksheet that comes with this book teaches you how to take it one step further and tailor the data more precisely to the unique aspects of your project.

    We could even expand our page weight budget by establishing carbon budgets as well with the ability to calculate carbon emissions for our projects. CO2 is not a metric commonly used in web projects, we’re more familiar with kilobytes and megabytes, and can fairly easily look at design options and files to assess how big they are. Although translating that into carbon adds a layer of abstraction that isn’t as intuitive, carbon budgets do focus our minds on the main thing we’re trying to reduce, and this is in line with the main goal of sustainable web design: reducing carbon emissions.

    Browser Energy

    Transfer of data might be the simplest and most complete analog for energy consumption in our digital projects, but by giving us one number to represent the energy used in the data center, the telecoms networks, and the end user’s devices, it can’t offer us insights into the efficiency in any specific part of the system.

    One part of the system we can look at in more detail is the energy used by end users ‘ devices. The computational burden is increasingly shifting from the data center to the users ‘ devices, whether they are phones, tablets, laptops, desktops, or even smart TVs, as front-end web technologies advance. Modern web browsers allow us to implement more complex styling and animation on the fly using CSS and JavaScript. Additionally, JavaScript libraries like Angular and React make it possible to create applications where the” thinking” process is performed either partially or completely in the browser.

    All of these advances are exciting and open up new possibilities for what the web can do to serve society and create positive experiences. However, more data is processed in a web browser, which means more energy is used by the user’s devices. This has implications not just environmentally, but also for user experience and inclusivity. Applications that put a lot of processing power on a user’s device unintentionally exclude users with older, slower devices and make the batteries on phones and laptops drain more quickly. Furthermore, if we build web applications that require the user to have up-to-date, powerful devices, people throw away old devices much more frequently. This not only harms the environment, but it places a disproportionate financial burden on the poorest members of society.

    In part because the tools are limited, and partly because there are so many different models of devices, it’s difficult to measure website energy consumption on end users ‘ devices. The Energy Impact monitor inside the Safari browser’s developer console ( Fig. 2.5 ) is one of the tools we currently use.

    You know what happens when your computer’s cooling fans start spinning so frantically that you suspect it might take off when you load a website? That’s essentially what this tool is measuring.

    It uses these figures to create an energy impact rating and shows how much CPU is used and how long it takes to load the web page. It doesn’t give us precise data for the amount of electricity used in kilowatts, but the information it does provide can be used to benchmark how efficiently your websites use energy and set targets for improvement.

  • A Content Model Is Not a Design System

    A Content Model Is Not a Design System

    Do you recall the days when having a fantastic site was sufficient? Nowadays, people are getting answers from Siri, Google seek fragments, and mobile applications, not only our websites. Forward-thinking companies have adopted an holistic information strategy whose goal is to reach audiences across a variety of digital channels and platforms.

    How can a content management system ( CMS ) be set up to reach your current and future audience? I learned the hard way that creating a content model—a concept of information types, attributes, and relationships that let people and systems understand content—with my more comfortable design-system wondering would collapse my patient’s holistic information strategy. By developing content versions that are conceptual and even join related content, you can avoid that result.

    I just had the opportunity to direct the CMS application for a Fortune 500 company. The customer was excited by the benefits of an holistic information plan, including material modify, multichannel marketing, and robot delivery—designing content to be comprehensible to bots, Google knowledge panels, snippets, and voice user interfaces.

    A content type is essential for an omnichannel information strategy, and the model needed conceptual types, which are types of types that are categorized according to their meaning rather than their presentation. Our aim was to allow artists to create original content and use it where necessary. However, as the project progressed, I realized that the entire team had to be aware of a new style in order to support material reuse on the level that my customer needed.

    Despite our best purposes, we kept drawing from what we were more common with: design techniques. Unlike web-focused information strategies, an holistic information strategy doesn’t rely on WYSIWYG equipment for design and structure. Our inclination to approach the material model using our well-known design-system thinking consistently stifled our attention from one of the main objectives of a willing model: delivering content to audiences across multiple marketing channels.

    Two fundamental tenets govern a successful information type

    We needed to explain to our designers, developers, and stakeholders that we were undertaking a very unique task from their earlier web projects, where it was common for everyone to view content as visible building blocks that fit into layouts. Because it made the layouts feel more recognizable, the previous approach was more intuitive, at first, at least initially. We learned two guiding principles that helped the team comprehend how a willing model and the design processes we were familiar with were:

    1. Instead of design, content models may establish semantics.
    2. And glad models may connect elements that belong together.

    Conceptual material models

    A conceptual content type uses form and attribute names that reflect the content’s intended purpose and not how it will be displayed. For instance, in a nonsemantic design, groups may make varieties like teasers, press blocks, and cards. These types may make it simple to present information, but they do not aid in understanding the meaning of the content, which would have opened the door to the content presented in each marketing channel. In contrast, a conceptual content model employs type names like product, service, and testimony to allow for each supply route to interpret the information and use it as necessary.

    A great place to start when creating a conceptual content concept is by reviewing the types and qualities that Schema has defined. com, a community-driven tool for type meanings that are comprehensible to platforms like Google search.

    A semantic information model has many advantages:

      A semantic material type decouples information from its presentation but that teams can change the website’s design without having to restructure its content, even if your team doesn’t worry about omnichannel content. In this way, content can withstand disruptive website redesigns.
    • A competitive advantage can also be gained by a semantic content model. by including schema-based structured data. org’s types and properties, a website can provide hints to help Google understand the content, display it in search snippets or knowledge panels, and use it to answer voice-interface user questions. Potential visitors could access your content without ever walking into your website.
    • Beyond those practical advantages, you’ll also require an omnichannel content delivery model. Delivery channels must be able to comprehend the same content in order to use it across multiple marketing channels. For instance, if your content model provided a list of questions and answers, it could be easily displayed on a frequently asked questions ( FAQ ) page as well as be used by a bot to answer frequently asked questions.

    For example, using a semantic content model for articles, events, people, and locations lets A List Apart provide cleanly structured data for search engines so that users can read the content on the website, in Google knowledge panels, and even with hypothetical voice interfaces in the future.

    Content models that connect

    Instead of slicing up related content across disparate content components, I’ve come to the realization that the best models are those that are semantic and also connect related content components ( such as a FAQ item’s question and answer pair ). A good content model connects pieces of content that ought to be preserved so that multiple delivery channels can use it without having to assemble those pieces separately.

    Write an essay or article about it. An article’s meaning and usefulness depends upon its parts being kept together. Would one of the headings or paragraphs have any significance on their own if the entire article were not included? Our well-known design-system thinking on our project frequently led us to want to develop content models that would divide content into distinct chunks to fit the web-centric layout. This had a similar effect to an article that had its headline removed. Content that belonged together became challenging to manage and nearly impossible for multiple delivery channels to understand because we were cutting content into separate pieces based on layout.

    To illustrate, let’s look at how connecting related content applies in a real-world scenario. A complex layout for a software product page that included multiple tabs and sections was presented by the client’s design team. The content model lacked instincts, so we had to follow our instincts. Shouldn’t we make adding multiple tabs in the future as simple and flexible as possible?

    Because our design-system instincts were so well-known, it appeared that we needed a “tab section” content type so that multiple tab sections could be added to a page. Each tab section would display various kinds of information. One tab might provide the software’s overview or its specifications. Another tab might provide a list of resources.

    Our tendency to divide the content model into “tab section” pieces would have resulted in a cumbersome editing process, as well as unnecessarily complex content that couldn’t have been digested by additional delivery channels. How would another system have resorted to counting tab sections and content blocks, for instance, if it had been able to identify a product’s “tab section” when referring to its specifications or resource list? This would have prevented the tabs from ever being rearranged, and it would have required adding logic to each other delivery channel to interpret the layout of the design system. Additionally, it would have been difficult to migrate to a new content model in response to the new page redesign if the customer had decided against displaying this content in a tab layout.

    We had a breakthrough when we discovered that our customer had a specific purpose in mind for each tab: it would reveal specific information such as the software product’s overview, specifications, related resources, and pricing. Our desire to concentrate on the visually appealing and well-known had obscured the design’s purpose once implementation began. With a little digging, it didn’t take long to realize that the concept of tabs wasn’t relevant to the content model. What was important was the meaning of the content they were planning to display in the tabs.

    In fact, the customer could have chosen to display this content elsewhere in a different manner, without tabs. In response to this realization, we created content types for the software product based on the meaningful attributes the client wanted to display on the web. There were obvious semantic attributes like name and description as well as rich attributes like screenshots, software requirements, and feature lists. The software’s product information stayed together because it wasn’t sliced across separate components like “tab sections” that were derived from the content’s presentation. Any delivery channel—including future ones—could understand and present this content.

    Conclusion

    In this omnichannel marketing project, we discovered that the best way to maintain our content model was to ensure that it was semantic ( with type and attribute names that reflected the content’s meaning ) and that it preserved content that belonged to be together ( instead of fragmenting it ). These two ideas made it easier for us to shape the content model based on the design. Remember: If you’re developing a content model to support an omnichannel content strategy, or even if you just want to make sure Google and other interfaces understand your content, remember:

    • A design system isn’t a content model. Team members may be persuaded to combine them and have their content model resemble their design system, so you should guard the semantic and contextual integrity of the content strategy throughout the entire implementation process. Without the use of a magic decoder ring, every delivery channel will be able to consume the content.
    • If your team is having trouble making this transition, Schema can still offer some of the advantages. org–based structured data in your website. The benefit of search engine optimization is a compelling reason on its own, even if additional delivery channels aren’t on the horizon in the near future.
    • Remind the team that removing the content model from the design will allow them to update the designs more quickly because content migration costs won’t be prohibitive. They will be prepared for the upcoming big thing, and they will be able to create new designs without compromising the compatibility between the content and the design.

    By firmly defending these ideas, you’ll help your team view content as the most important component of your user experience and as the most effective way to engage with your audience.