In reading Joe Dolson’s recent piece on the intersection of AI and accessibility, I absolutely appreciated the skepticism that he has for AI in general as well as for the ways that many have been using it. In fact, I’m very skeptical of AI myself, despite my role at Microsoft as an accessibility innovation strategist who helps run the AI for Accessibility grant program. As with any tool, AI can be used in very constructive, inclusive, and accessible ways; and it can also be used in destructive, exclusive, and harmful ones. And there are a ton of uses somewhere in the mediocre middle as well.
I’d like you to consider this a “yes… and” piece to complement Joe’s post. I’m not trying to refute any of what he’s saying but rather provide some visibility to projects and opportunities where AI can make meaningful differences for people with disabilities. To be clear, I’m not saying that there aren’t real risks or pressing issues 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 hopes that we’ll get there one day.
Alternative text#section2
Joe’s piece spends a lot of time talking about computer-vision models generating alternative text. He highlights a ton of valid issues with the current state of things. And while computer-vision models continue to improve in the quality and richness of detail in their descriptions, their results aren’t great. As he rightly points out, the current state of image analysis is pretty poor—especially for certain image types—in large part because current AI systems examine images in isolation rather than within the contexts that they’re in (which is a consequence of having separate “foundation” models for text 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. Still, I still think there’s potential in this space.
As Joe mentions, human-in-the-loop authoring of alt text should absolutely be a thing. And if AI can pop in to offer a starting point for alt text—even if that starting point might be a prompt saying What is this BS? That’s not right at all… Let me try to offer a starting point—I think that’s a win.
Taking things a step further, if we can specifically train a model to analyze image usage in context, it could help us more quickly identify which images are likely to be decorative and which ones likely require a description. That will help reinforce which contexts call for image descriptions and it’ll improve authors’ efficiency toward making their pages more accessible.
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 suppose that you came across a chart whose description was simply the title of the chart and the kind of visualization it was, such as: Pie chart comparing smartphone usage to feature phone usage among US households making under $30,000 a year. (That would be a pretty awful alt text for a chart since that would tend to leave many questions about the data unanswered, but then again, let’s suppose that that was the description that was in place.) If your browser knew that that image was a pie chart (because an onboard model concluded this), imagine a world where users could ask questions like these about the graphic:
- Do more people use smartphones or feature phones?
- How many more?
- Is there a group of people that don’t fall into either of these buckets?
- 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 could also be useful in educational contexts to help people who can see these charts, as is, to understand the data in the charts.
Taking things a step further: What if you could ask your browser to simplify a complex chart? What if you could ask it to isolate a single line on 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 swap colors for patterns? Given these tools’ chat-based interfaces and our existing ability to manipulate images in today’s AI tools, that seems like a possibility.
Now imagine a purpose-built model that could extract the information 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#section3
Safiya Umoja Noble absolutely hit the nail on the head when she titled her book Algorithms of Oppression. While her book was focused on the ways that search engines reinforce racism, I think that it’s equally true 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 lot of this stems from a lack of diversity among the people who shape 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 are an employment network for neurodivergent people. They use an algorithm to match job seekers with potential employers based on over 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. On the employer side, it considers each work environment, communication factors related to each job, and the like. As a company run by neurodivergent folks, Mentra made the decision to flip the script when it came to typical employment sites. 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 the creation of algorithms, that can reduce the chances that these algorithms will inflict harm on their communities. That’s why diverse teams are so important.
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 example, if you were to follow a bunch of nondisabled white male academics who talk about AI, it could suggest that you follow academics who are disabled or aren’t white or aren’t male who also talk about AI. 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#section4
If I weren’t trying to put this together between other tasks, I’m sure that I could go on and on, providing all kinds of examples of how AI could be used to help people with disabilities, but I’m going to make this last section into a bit of a lightning round. 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. 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 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 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 have plans to expand this to other conditions as the project progresses. This research will result in more inclusive data sets that will let more people with disabilities use voice assistants, dictation software, and voice-response services as well as control their computers and other devices more easily, using only their voice.
- Text transformation. The current generation of LLMs is quite capable of adjusting existing text content without injecting hallucinations. This is hugely empowering for people with cognitive disabilities who may benefit from text summaries or simplified versions of text or even text that’s prepped for Bionic Reading.
The importance of diverse teams and data#section5
We need to recognize that our differences matter. Our lived experiences are influenced by the intersections of the identities that we exist in. 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 need to be represented in the data that we use to train new models, and the folks who contribute that valuable information need to be compensated for sharing it with us. Inclusive data sets yield more robust models that foster more equitable outcomes.
Want a model that doesn’t demean or patronize or objectify people with disabilities? Make sure that you have content about disabilities that’s authored by people with a range of disabilities, and make sure that that’s well represented in the training data.
Want a model that doesn’t use ableist language? You may be able to use existing data sets to build a filter that can intercept and remediate ableist language before it reaches readers. That being said, when it comes to sensitivity reading, AI models won’t be replacing human copy editors anytime soon.
Want a coding copilot that gives you accessible recommendations from 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.
In speaking of alt text generators and context, the larger site context must also be evaluated. For example, a hero image on an ecommerce site might have alt text similar to “Woman hiking in the forest wearing our signature wool sweater in pacific blue”. That alt text is providing the same intended context as the visual, to highlight a specific product, while also being accurate. Generative alt text would miss the mark on intent and context of surrounding elements, of the page, of the whole site, etc. Now, in some future state, there may be the ability to build a model that takes additional inputs beyond the source image such as product images, product descriptions, etc. aligned to my example, then some of that context may be more accurately generated.
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There are many more potentials for a11y-focused Machine Learning(ML)! For instance, much work is being done on translating sign language to voice and I see lot of potential in training WCAG analysis from data from accessibility pros. There is also future where screen readers are re-imagined because speech-based UX becomes the dominate way of understanding the web, and every site is developed with speech styling (as we once integrated print styling). So many other projects come up when I speak to my ML colleagues! I would be happy to write an expansion on your expansion of Joe’s expansion! I see alt text AI as the quickest, most basic application of what will be an amazing future, led by screen-less AI revolution.
Great points about balancing AI risks and opportunities for disabilities! Especially loved the Mentra example. Transparency and user-centered design are also key for responsible AI development.
This post beautifully highlights the transformative power of AI in enhancing accessibility for individuals with disabilities.
From speech recognition to image captioning, the possibilities are endless.
It’s heartening to see technology evolving not just for efficiency’s sake but for inclusivity. Looking forward to seeing more innovations in this space!
Your insightful exploration of the opportunities for AI in accessibility truly resonates with the transformative potential of technology to empower and include individuals of all abilities.
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The application of artificial intelligence technology is becoming more and more common in our daily lives, including industrial production, medical technology and sex robots. I believe that our lives will get better and better because of AI technology!
Rant: I wonder when people are finally get it into their head that their isnt anything remotely “AI”. The correct term would be “Generative AI” at best!
BTT: Aside of all the hype, if we were to look at IAs or “generative AIs” as the actual modern realization of Intelligent Assistants (Siri would be a “modern” example, and Clippy one of the past), it certainly could become something helpful.
And if we look past “accessibility” and would realize “accessible” means: Better access to anything is good for everyone, not just “those handicapped poor people”, then it would even be better.
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Their insightful exploration of artificial intelligence’s capabilities in accessibility truly reflects the transformative power of technology to empower and include people of all abilities.
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Your perspective adds depth to the discussion on AI in accessibility, highlighting its potential for positive impact despite acknowledged risks. The examples provided, such as improving alt text generation and matching algorithms in employment networks, offer insight into how AI can empower people with disabilities. Emphasizing diverse teams and inclusive data sets, you underscore the importance of equity in shaping AI’s trajectory. Well-articulated and thought-provoking!|
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Thank you for sharing such an insightful article on the opportunities AI presents for accessibility. Your exploration of how AI can enhance alt text generation and improve matching algorithms in employment networks is particularly enlightening. It’s encouraging to see the potential of AI to empower individuals with disabilities while acknowledging the risks involved. Emphasizing the importance of diverse teams and inclusive data sets is crucial for ensuring equitable outcomes in AI development. Looking forward to seeing these innovations come to life!
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Your article offers a balanced perspective on AI’s potential in accessibility. While acknowledging the challenges, you highlight promising applications like voice preservation and text transformation. I’m particularly intrigued by the idea of AI-assisted alt text generation. How do you envision AI and human collaboration evolving to create more accurate and contextually relevant alt text in the future? Could this partnership lead to significant improvements in web accessibility?
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It’s encouraging to see the potential of AI to empower individuals with disabilities while acknowledging the risks involved. Emphasizing the importance of diverse teams and inclusive data sets is crucial for ensuring equitable outcomes in AI development.
I’m particularly intrigued by the idea of AI-assisted alt text generation. How do you envision AI and human collaboration evolving to create more accurate and contextually relevant alt text in the future? Could this partnership lead to significant improvements in web accessibility?
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I found this article deeply inspiring and necessary. At a time when discussions around AI often focus on its dangers, this perspective strikes a powerful balance by pairing critique with a constructive outlook. I especially value the concrete examples of how AI could radically improve the lives of people with disabilities—from more contextual alt text to personalized voice technologies. But what resonates most is the idea that true inclusion isn’t achieved through good intentions alone—it requires actively involving those who live these realities. If we want AI to be fairer, more diverse, and genuinely useful, we need to build it with everyone at the table.