arXiv AI October 12, 2026 By Mahika Phutane, Hayoung Jung, Matthew Kim, Tanushree Mitra, Aditya Vashistha

ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios

The paper "ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios" audits six large language models across 2,820 hiring scenarios that vary by disability, gender, nationality, and caste. It introduces the ABLEIST framework—comprising five ableism-specific and three intersectional harm metrics—to detect subtle biases. The study finds that many state‑of‑the‑art models exhibit significant ABLEIST harms, especially for gender- and caste-marginalized disabled candidates, exposing blind spots in current safety tools.

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