Auditing LLM Responses to Harmful Stereotypes Targeting Mental Health Groups

ACL ARR 2026 January Submission10203 Authors

Keywords: Large Language Models, Bias and stereotype, Mental health

Abstract: Large Language Models (LLMs) can exhibit imbalanced biases against vulnerable groups, but how they rationalize stereotypes and rights restrictions targeting mental health entities remains underexplored. We audit a broad suite of open-weight LLMs on stereotype-justification prompts tied to mental health identities. We find that several widely used models endorse harmful stereotypes when explicitly asked to justify them, with endorsement varying across model families, versions, and mental health conditions. Finally, we show that widely used harmful-content evaluation and moderation frameworks often miss these nuanced, discriminatory responses, highlighting a gap in current AI safety evaluation for mental health groups.

Paper Type: Long

Research Area: Ethics, Bias, and Fairness

Research Area Keywords: Computational Social Science, Cultural Analytics, and NLP for Social Good

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 10203