Editing AI for Accessibility (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
AI accessibility applications work at the intersection of assistive technology and general-purpose AI. Many AI advances originally developed for general purposes have transformative accessibility applications:

**Speech recognition** (originally for dictation) → real-time captions for deaf users, speech-to-text for motor-impaired users who can speak but not type. Whisper and cloud ASR APIs have dramatically improved caption quality and reduced cost, making automatic captions standard on video platforms.

**Computer vision** (originally for classification) → image descriptions for blind users, text reading (OCR), face/scene recognition, navigation assistance. Microsoft's Seeing AI app uses multiple CV models to describe scenes, read text, recognize faces, scan products (via barcode), and identify currencies — all through a smartphone camera. Google Lookout provides similar functionality.

**NLP** (originally for chatbots) → simplified text for cognitive accessibility (rewriting complex documents in plain language), AAC word prediction, screen reader improvements (better UI element summarization), and AI-assisted writing for dyslexia.

**Eye tracking + AI prediction**: Users with severe motor impairments can control computers entirely with their eyes. Traditional eye-tracking interfaces are slow — scanning through menus. AI word prediction and sentence completion dramatically accelerates communication by predicting what the user intends before they complete selection.

**The curse of the last mile**: Despite AI's potential, accessibility features are frequently afterthoughts. Models trained on non-disabled user data perform worse for users with disabilities — ASR accuracy for speakers with dysarthria (speech differences from conditions like cerebral palsy) is dramatically lower. This is both a technical and ethical challenge.
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