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Evidence record 17769 · automatically gathered

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

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. Although prior work has examined intrinsic representational bias and unfair downstream behavior separately, it remains unclear whether mitigating intrinsic bias leads to fairer downstream outcomes. We introduce Fairness-Aware Concept Unlearning (FACU), a model-level mitigation m

Record details

Published: 10 August 2026
Source: arXiv cs.CY
Category: Research
Topics: Bias & fairness
Retrieved: 10 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (10 August 2026), “Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs,” evidence record 17769, https://ethics.ai/record/17769 (originally published by arXiv cs.CY).

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