Papers with bias mitigation strategy

2 papers
White Men Lead, Black Women Help? Benchmarking and Mitigating Language Agency Social Biases in LLMs (2025.acl-long)

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Challenge: Social biases manifest in language agency, but there is no comprehensive benchmark for evaluating such biase in language models.
Approach: They propose a benchmark to evaluate language agency biases in large language models . they propose 'Mitigation via Selective Rewrite' to selectively revise parts of generated texts .
Outcome: The proposed language agency bias evaluation benchmark identifies gender, racial, and intersectional biases in 3 recent LLMs.
Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of Race (2025.acl-long)

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Challenge: et al., 2012) show value-aligned language models exhibit stereotypes in word association tasks . ignoring racial nuances can perpetuate subtle biases in LMs .
Approach: They propose a bias mitigation strategy that incentivizes representation of racial concepts in early model layers.
Outcome: The proposed approach incentivizes representation of racial concepts in early model layers . it reduces implicit bias by reducing the number of ambiguous inputs, the authors show .

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