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
this https URL

数据收集中的历史性歧视对弱势群体和个体产生了影响。现有的公平机器学习研究主要致力于减轻模型预测中的歧视倾向，但对于如何追踪数据中存在的偏见却付出了较少的努力，尽管这对公平机器学习的透明度和可解释性非常重要。为了填补这一空白，我们研究了一个新的问题：发现在训练数据中反映偏见 / 偏见的样本。基于现有的公平性概念，我们提出了一个样本偏见准则，并提出了衡量和抵消样本偏见的实用算法。得出的偏见分数提供了历史偏见在数据中的直观样本级归因和说明。在此基础上，我们通过基于样本偏见的最小数据编辑设计了两种公平机器学习策略。它们可以在最小或零的预测效用损失的情况下，减轻群体和个体的不公平现象。对多个真实世界数据集的大量实验和分析证明了我们方法在解释和减轻不公平方面的有效性。代码可在此 URL 获取：https://URL