In machine learning systems, bias mitigation approaches aim to make outcomes
fairer across privileged and unprivileged groups. Bias mitigation methods work
in different ways and have known "waterfall" effects, e.g., mitigating bias at
one place may manifest bias elsewhere. In this paper, we aim to characterise
impacted cohorts when mitigation interventions are applied. To do so, we treat
intervention effects as a classification task and learn an explainable
meta-classifier to identify cohorts that have altered outcomes. We examine a
range of bias mitigation strategies that work at various stages of the model
life cycle. We empirically demonstrate that our meta-classifier is able to
uncover impacted cohorts. Further, we show that all tested mitigation
strategies negatively impact a non-trivial fraction of cases, i.e., people who
receive unfavourable outcomes solely on account of mitigation efforts. This is
despite improvement in fairness metrics. We use these results as a basis to
argue for more careful audits of static mitigation interventions that go beyond
aggregate metrics.

机器学习系统中，偏见缓解方法旨在使特权组和非特权组的结果更加公平。本文旨在对应用缓解干预措施时受影响的群体进行特征化，将干预效果视为分类任务，并学习可解释的元分类器以识别具有改变结果的群体。我们检验了在模型生命周期的不同阶段工作的一系列偏见缓解策略，并在实证上证明了我们的元分类器能够揭示受影响的群体。此外，我们展示了所有测试过的缓解策略对部分人群产生了负面影响，即仅因缓解措施而获得不利结果的人群。尽管在公平度量指标上有所改善，但我们使用这些结果作为基础，主张对超出聚合指标的静态缓解干预措施进行更加谨慎的审查。