IFAAB audit finds GPT-4o financial-advice gaps for Indian personas
Male-labelled profiles received 60.1% higher initial resources and 46.8% higher post-advice resources than female-labelled ones.
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Male-labelled profiles received 60.1% higher initial resources and 46.8% higher post-advice resources than female-labelled ones.
Researchers propose dual minimal pairs and a mutual-information metric for more reliable LLM stereotype evaluation.
Researchers propose auditing LLM bias via relative hidden-state representations, detecting bias increases with 3-50x less compute than output-level benchmarks.
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