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Investigating Counterfactual Unfairness in LLMs towards Identities through Humor

Researchers exploit counterfactual humor generation to measure identity-based bias in LLMs, revealing systematic fairness failures across demographic groups.

Wednesday, April 22, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.CL (Computation & Language)BY sys://pipeline

Research paper investigating how large language models exhibit unfairness toward different identities using counterfactual analysis. The authors employ humor as a test domain to examine identity-related bias patterns in LLM outputs.

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