Accessibility and AI
AI offers enormous potential for accessibility — and real risks if implemented carelessly.
Automated captioning, predictive text, and voice interfaces already make digital environments more inclusive.
AI-driven accessibility testing tools can identify common issues at scale. But algorithms are only as inclusive as the
data behind them.
When AI models overlook users with disabilities, personalization can unintentionally exclude. For example,
recommendation systems might suppress accessible versions of content if they don’t generate as many clicks.
To align AI with accessibility:
•
Train models on diverse user data and assistive tech interactions.
•
Keep accessibility features visible and controllable by users.
•
Regularly audit AI-driven experiences for bias, accuracy, and unintended exclusion – for example, checking automated
captions or alt text for errors that could impact comprehension.
•
Combine automation with human review to ensure real-world usability.
AI should amplify inclusion, not replace empathy. When used responsibly, it accelerates accessibility progress across
massive digital ecosystems.
Accessibility Beyond Compliance
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