  Data collected in the real world often encapsulates historical discrimination
against disadvantaged groups and individuals. Existing fair machine learning
(FairML) research has predominantly focused on mitigating discriminative bias
in the model prediction, with far less effort dedicated towards exploring how
to trace biases present in the data, despite its importance for the
transparency and interpretability of FairML. To fill this gap, we investigate a
novel research problem: discovering samples that reflect biases/prejudices from
the training data. Grounding on the existing fairness notions, we lay out a
sample bias criterion and propose practical algorithms for measuring and
countering sample bias. The derived bias score provides intuitive sample-level
attribution and explanation of historical bias in data. On this basis, we
further design two FairML strategies via sample-bias-informed minimal data
editing. They can mitigate both group and individual unfairness at the cost of
minimal or zero predictive utility loss. Extensive experiments and analyses on
multiple real-world datasets demonstrate the effectiveness of our methods in
explaining and mitigating unfairness. Code is available at
https://github.com/ZhiningLiu1998/AIM.
