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- W4386242309 abstract "When bias mitigation methods are applied to make fairer machine learning models in fairness-related classification settings, there is an assumption that the disadvantaged group should be better off than if no mitigation method was applied. However, this is a potentially dangerous assumption because a “fair” model outcome does not automatically imply a positive impact for a disadvantaged individual—they could still be negatively impacted. Modeling and accounting for those impacts is key to ensure that mitigated models are not unintentionally harming individuals; we investigate if mitigated models can still negatively impact disadvantaged individuals and what conditions affect those impacts in a loan repayment example. Our results show that most mitigated models negatively impact disadvantaged group members in comparison to the unmitigated models. The domain-dependent impacts of model outcomes should help drive future bias mitigation method development." @default.
- W4386242309 created "2023-08-30" @default.
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- W4386242309 date "2023-08-08" @default.
- W4386242309 modified "2023-10-01" @default.
- W4386242309 title "Not So Fair: The Impact of Presumably Fair Machine Learning Models" @default.
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- W4386242309 doi "https://doi.org/10.1145/3600211.3604699" @default.
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