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• Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter

  Xinru Tang, Jingjin Li, Shaomei Wu · 2026 · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26)

  Tang, Li, and Wu present the first study to push the 'disability-first' principle beyond dataset collection and into the dataset annotation stage of the AI pipeline. Their case is stuttered speech: despite a growing number of stuttering datasets (FluencyBank, UCLASS, KSoF,…

  AI dataset annotation · stuttering · speech recognition · disability-first design · embodied knowledge

• Surveilling Suitability: How AI Hiring Interviews Impact Job Seekers with Disabilities

  Vaishnav Kameswaran, Valentina Hong, Jazmin Clark, Yu Hou, Hal Daumé III, Katie Shilton · 2026 · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26)

  This CHI 2026 paper reports a qualitative study of how AI-driven hiring interview platforms — asynchronous video interview tools (e.g., HireVue) that use AI to score candidates on facial expressions, vocal cues, and behavioural data — are perceived and experienced by job seekers…

  disability · AI hiring · surveillance · algorithmic bias · employment

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