Knowing the causes of a model's unfairness helps practitioners better
understand their data and algorithms. This is an important yet relatively
unexplored task. We look into this problem through the lens of the training
data - one of the major sources of unfairness. We ask the following questions:
how would a model's fairness performance change if, in its training data, some
samples (1) were collected from a different (e.g. demographic) group, (2) were
labeled differently, or (3) some features were changed? In other words, we
quantify the fairness influence of training samples by counterfactually
intervening and changing samples based on predefined concepts, i.e. data
attributes such as features (X), labels (Y), or sensitive attributes (A). To
calculate a training sample's influence on the model's unfairness w.r.t a
concept, we first generate counterfactual samples based on the concept, i.e.
the counterfactual versions of the sample if the concept were changed. We then
calculate the resulting impact on the unfairness, via influence function, if
the counterfactual samples were used in training. Our framework not only helps
practitioners understand the observed unfairness and repair their training
data, but also leads to many other applications, e.g. detecting mislabeling,
fixing imbalanced representations, and detecting fairness-targeted poisoning
attacks.

通过预定义的概念进行反事实干预，通过影响函数计算培训样本对模型的不公平的影响，帮助实践者理解观察到的不公平现象并修复其培训数据，从而实现检测错误标记、修复不平衡表示、检测针对公平性的污染攻击等其他应用。