No, ‘AI’ Will Not Fix Accessibility

No, ‘AI’ Will Not Fix Accessibility

June 8, 2023; 0 Comments
In recent years, a series of new technologies have provided better experiences and outcomes for disabled users. Collectively branded “Artificial Intelligence”, the two biggest breakthroughs have been in computer vision and large language models (LLM).

The former, computer vision, allows a computer to describe an image based on extensive training on massive image data-sets, human tagging, and ongoing tweaking. This is well beyond the relatively ancient technology of optical character recognition (OCR), which allows your computer to transcribe the text it finds in a picture. Images that were empty voids to blind users could finally be useful.

Microsoft’s Seeing AI is just one example, allowing people to use an app on their phone to identify people, differentiate currency, navigate a refrigerator, and more. Be My Eyes, which has relied on sighted volunteers for the same tasks, is trialing its Virtual Volunteer. This would give users more privacy and immediate responses.

Similarly, LLMs can help readers distill a complex or verbose article to an abstract or different reading level. They can also help writers when anxiety, language barriers, dyslexia, and more might make working difficult. We can generate reasonable quality captions and transcripts on our own computers, often a significant improvement from the “craptions” of yore.

But…

Large language models are habitual liars. Meanwhile, automated image descriptions aren’t much better. To give them the benefit of the doubt, perhaps these tools simply lack context.

As image tools get better at describing every detail of a picture, as language models do a better job of conveying an emoji-laden tweet in actual words, they are still not the authors of that content. That have no sense of why it was created. They cannot tell you that a series of vertical lines is meant to signify a wall in a meme. They don’t know that the gag of a photo of a Rice Krispie treat with a birthday candle is the power drill in the background.

Now let’s consider the code that holds all that content.

We have witnessed accessibility overlay vendors for years claim they are deploying AI technology and still failing users miserably. With their influx of funding and lofty promises, it should be done by now.

Forgetting the snake oil of overlay vendors, consider tools like GitHub Copilot, which claims to be “your AI pair programmer”. These work by leaning on the code of thousands and thousands of projects to build its code auto-complete features.

When you copy broken patterns you get broken patterns. And I assure you, GitHub, Google, Apple, Facebook, Amazon, stacks of libraries and frameworks, piles of projects, and so on, are rife with accessibility barriers.

Let’s assume those coding tools eventually get cleaned up and will output idealized, conformant, accessible code. How will they account for platform bugs? No tool is stopping you from using CSS display properties on tables today. No accessibility overlay is rebuilding them dynamically to visually look the same while avoiding these long-documented bugs.

Because…

Accessibility is about people. It is not strictly a technical problem to be solved with code. It is not the approximation of human-like ramblings produced by complex algorithms generally branded as “AI”.

LLM chatbots and computer-generated images are already creating an uncanny valley of spam. We don’t need them to contribute to the analogous uncanny accessibility already extant on the web.

Creating a broken MVP because “AI” will be able to fix it later is lazy. Believing something is accessible because the “AI” said so is shoddy.

When we target output versus outcomes, we are failing our friends, our family, our community, and our future selves. We are excluding fellow humans while we try to absolve ourselves of responsibility by laying it at the feet of a black box its very makers warn will ruin the world.

Tags

accessibility, rant, standards, usability