ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios

A research audit of six large language models across 2,820 hiring scenarios evaluated intersectional disability bias, focusing on understudied non-Western contexts involving caste, gender, and nationality. The study introduces the ABLEIST framework, establishing five ableism-specific and three intersectional harm metrics to quantify biases such as ableism, inspiration, superhumanization, and tokenism. Note that the source abstract is partially truncated at its conclusion.

This research matters because organizations increasingly deploy LLMs in recruitment workflows without evaluating non-Western intersectional biases, creating legal, reputational, and ethical exposures. It highlights that standard AI bias assessments fail to capture specific forms of marginalization affecting people with disabilities across international labor markets.

Large language models (LLMs) perpetuate identity-based discrimination in high-stakes hiring scenarios, particularly affecting people with disabilities (PwD).

Forward consideration, not a verified fact.