arXiv Machine Learning August 10, 2026 By Mina Arzaghi, Alireza Dehghanpour Farashah, Florian Carichon, Jean-Fran\c{c}ois Plante, Golnoosh Farnadi

Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs

Read the original on arXiv Machine Learning →

arXiv:2509. 16462v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economic disparities.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Measuring Fairness in Large Audio Language Models via Semantic-Aware Bias Estimation

Beyond Procedure: Substantive Fairness in Conformal Prediction

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