Opportunities for Ai in Accessibility

Opportunities for AI in Accessibility

Personal Note:

I think this article falls short in capturing the full impact AI will have on accessibility, not only for people with disabilities but also for those who, despite not having recognized disabilities, face accessibility issues. This includes tasks ranging from reading cursive handwriting to understanding non-native speakers who use dialects.

Moreover, I believe there are numerous additional areas of accessibility that the article didn’t address or mention. For example, the sound recognition feature of iOS saves lives by alerting users to critical sounds they can’t hear, like a smoke detector alarm. Likewise, large language models (LLMs) are incredibly useful for people with ADHD (this was indirectly mentioned int the article (props)) by transforming texts into bullet points or adding better text breaks for improved readability. People with dyslexia also benefit significantly from LLMs like GPT-4, which can correct typos and grammar errors in ways far superior to any non-AI solution.

There are many more examples like these, and we’re only scratching the surface of what’s possible and how AI is being used to make the world more accessible in unexpected ways.

TLDR:

1. The author acknowledges Joe Dolson’s skepticism towards AI, particularly in the realm of accessibility, but aims to highlight where AI can positively impact people with disabilities (PwD) without disputing Dolson’s concerns.
2. The discussion includes the potential for AI in generating alternative text for images, emphasizing the need for improvement in contextual understanding and human-in-the-loop systems for better alt text authoring.
3. The article suggests AI’s future capabilities could revolutionize access for PwD by enabling interactive queries on images, simplifying complex charts for better understanding, and even converting visual data into more accessible formats.
4. It addresses the significant issue of bias in algorithms, proposing that increased diversity in algorithm development could mitigate harm and benefit PwD, with examples like the employment platform Mentra designed for neurodivergent individuals.
5. The author argues for the importance of diverse teams and data in creating more inclusive AI systems, underscoring the potential of AI to empower PwD while acknowledging the ongoing risks and advocating for responsible development and use.