When 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 fairness 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 mitigation method development.
|Title of host publication||Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society|
|Place of Publication||Montreal, Canada|
|Publication status||Published - Aug 2023|
- Machine Learning
- Synthetic data