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. In fact, I’m very skeptical of AI myself, despite my role at Microsoft as an accessibility technology strategist who helps manage the AI for Accessibility award program. As with any device, AI can be used in very positive, equitable, and visible ways, as well as in destructive, unique, and harmful ways. Additionally, there are a bit of uses in the subpar center as well.

I’d like you to consider this a “yes … and” piece to complement Joe’s post. I’m just trying to contradict what he’s saying, but I’m just trying to give some context to initiatives and opportunities where AI can make a difference for people with disability. 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; instead, I want to take a moment to talk about what’s possible so that we can get there one day.

Other words

Joe’s article spends a lot of time addressing computer-vision types ‘ ability to create alternative words. He raises a lot of valid points about the state of the world 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 image 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 ( should probably have descriptions ) and those that are purely decorative ( couldn’t possibly need a description ) either. However, I still think there’s possible in this area.

As Joe points out, human-in-the-loop editing of ctrl text should definitely be a factor. And if AI can intervene and provide a starting point for alt text, even if the quick 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 teach a design to consider image usage in context, it might be able to help us more swiftly distinguish between images that are likely to be attractive and those that are more descriptive. That will help clarify which situations require image descriptions, and it will increase authors ‘ effectiveness in making their sites more visible.

While complex images—like graphs and charts—are challenging to describe in any sort of succinct way ( even for humans ), the image example shared in the GPT4 announcement points to an interesting opportunity as well. Let’s say you came across a map that was simply the description of the chart’s title and the type of representation it was: Pie map comparing smartphone usage to have phone usage in US households earning 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. ) If your website knew that that picture was a pie graph ( because an ship model concluded this ), imagine a world where people could ask questions like these about the creative:

Would more people use smartphones or other types of phones?
How many more?
Is there a group of people that don’t fall into either of these pots?
How many people are that?

For a time, the chance to learn more about images and data in this way may be innovative for people with low vision and blindness as well as for those with various forms of color blindness, mental disabilities, and other issues. It could also be helpful in education settings to help people who can see these figures, as is, to understand the data in the figures.

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 graph? What if you could request your website to transform the colors of the various ranges to work better for type of colour blindness you have? What if you asked it to switch colours in favor of habits? That seems like a prospect given the chat-based interface and our current ability to manipulate photos in the AI tools of today.

Now imagine a purpose-built unit that was extract the information from that table and turn it to another style. Perhaps it could convert that pie chart (or, better yet, a series of pie charts ) into more usable ( and useful ) formats, like spreadsheets, for instance. That would be incredible!

Matching systems

When Safiya Umoja Noble chose to write her guide Algorithms of Oppression, she hit the nail on the head. Although her book focused on the way that search engines can foster racism, I believe it’s equally true that all machine types have the potential to foster issue, prejudice, and hatred. Whether it’s Twitter always showing you the latest tweet from a bored billionaire, YouTube sending us into a Q-hole, or Instagram warping our ideas of what natural bodies look like, we know that poorly authored and maintained algorithms are incredibly harmful. A large portion of this is a result of a lack of diversity in the people who design and construct them. However, when these platforms are built with inclusive features in mind, there is real potential for algorithm development to help people with disabilities.

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

When more people with disabilities are involved in developing algorithms, this can lower the likelihood that these algorithms will harm their communities. That’s why diverse teams are so crucial.

Imagine that a social media company’s recommendation engine was tuned to analyze who you’re following and if it was tuned to prioritize follow recommendations for people who talked about similar things but who were different in some key ways from your existing 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 followed its advice, you might be able to understand what is happening in the AI field more fully and nuancedly. 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 assist 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. In no particular order:

voice recognition Researchers like those in the Speech Accessibility Project are paying people with disabilities for their help in collecting recordings of people with atypical speech. As I type, they are currently hiring people with Parkinson’s and related conditions, and they intend to expand this list as the project develops. More people with disabilities will be able to use voice assistants, dictation software, and voice-response services as a result of this research, which will result in more inclusive data sets that will enable them to use their computers and other devices more easily and with just their voices.
Text transformation. LLMs of the current generation are quite capable of changing text without creating 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 importance of diverse teams and 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 it need to 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 has been written by people with a variety of disabilities in the training data.

Want a model that uses ableist language without using it? You may be able to use existing data sets to build a filter that can intercept and remediate ableist language before it reaches readers. Despite this, AI models won’t soon replace human copy editors when it comes to sensitivity reading.

Want a copilot for coding that provides recommendations that are accessible after the jump? Train it on code that you know to be accessible.

I have no doubts about how dangerous AI can and will be for people today, tomorrow, and for the rest of the world. However, I also think that we can acknowledge this and make thoughtful, thoughtful, 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 supporting the development of this article, Ashley Bischoff for providing me with invaluable editorial support, and of course, Joe Dolson for the prompt.