  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.
