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 produce real, positive impacts on people with disabilities. I want to take some time to talk about what’s possible in hope that we’ll get there one day. I’m no saying that there aren’t real challenges or pressing problems with AI that need to be addressed; there are.
Other text
Joe’s article spends a lot of time addressing computer-vision models ‘ ability to create other 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. As he rightly points out, the state of image research is currently very poor, especially for some graphic types, in large part due to the lack of context for which AI systems look at images ( which is a result of having separate “foundation” models for words analysis and picture 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 to provide a starting place for alt text, even if the swift may say What is this BS? That’s certainly 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 beautiful and those that are more descriptive. That will 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 moment, the chance to learn more about graphics and data in this way could be innovative for people who are blind and low vision as well as for those with different types of color blindness, cognitive impairments, and other issues. Putting aside the challenges of large language model ( LLM) hallucinations. It could also be useful in educational contexts to help people who can see these charts, as is, to understand the data in the charts.
What if you could ask your browser to make a complicated chart simpler? What if you asked it to separate a single line from a line graph? What if you could ask your browser to transpose the colors of the different lines to work better for form of color blindness you have? What if you could ask it to switch colors for patterns? That seems like a possibility given the chat-based interfaces and our current ability to manipulate images in modern AI tools.
Now imagine a purpose-built model that could extract the information from that chart and convert it to another format. 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 algorithms
When Safiya Umoja Noble chose to call 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 to be equally accurate to say that all computer models have the potential to amplify conflict, bias, and intolerance. 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 attributable to the lack of diversity in those who create and shape them. There is real potential for algorithm development when these platforms are built with inclusive features in, though.
Take Mentra, for example. They serve as a network of employment for people who are neurodivers. 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 traditional 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. Diverse teams are crucial because of this.
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 recommendations, you might learn more about 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 assist people with disabilities
If I weren’t attempting to combine this with other tasks, I’m sure I could go on and on, giving various examples of how AI could be used 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:
- preservation of voice You may be aware of the voice-prescribing options from Microsoft, Acapela, or others, or you may have seen the announcement for VALL-E or Apple’s Global Accessibility Awareness Day. It’s possible to train an AI model to replicate your voice, which can be a tremendous boon for people who have ALS ( Lou Gehrig’s disease ) or motor-neuron disease or other medical conditions that can lead to an inability to talk. This technology can also be used to create audio deepfakes, so it’s something we need to approach responsibly, but the technology has truly transformative potential.
- 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 actively recruiting people with Parkinson’s and related conditions, and they intend to expand this to other conditions as the project develops. 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 or 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. Inclusive data sets produce stronger models that promote more justifiable outcomes.
Want a model that doesn’t demean or patronize or objectify people with disabilities? Make sure that the training data includes information about disabilities written by people with a range of disabilities.
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 recomprehensible recommendations after the jump? Train it on code that you know to be accessible.
I have no doubts about how dangerous AI 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.





