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• Toward a taxonomy of negative outcomes from the use of AI-driven systems for people with disabilities

  Krishna Venkatasubramanian, Haven Hardie, Tina-Marie Ranalli · 2025 · ASSETS 2025: 27th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper presents the first systematic taxonomy of how AI-driven systems create negative outcomes specifically for people with disabilities. The authors searched eight publicly available AI incident databases — including AIAAIC, AIID, OECD AI Incident Monitor, and the Database…

  AI fairness · algorithmic bias · disability rights · AI harm · AI incident databases

• Examining and Mitigating Ability-bias in LLMs via Self-Reflection

  Neel Iyer, Akshita Jha, Alisha Pradhan · 2025 · Proceedings of the 22nd International Web for All Conference (W4A)

  This short paper investigates ability bias in large language models — the tendency of LLMs to encode and perpetuate stereotypical or discriminatory associations about people with disabilities. Using the Bias Benchmarking Questionnaire (BBQ) dataset, the authors administered…

  ability bias · ableism · LLM bias · debiasing · AI fairness

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