  Identifying the causes of a model's unfairness is an important yet relatively
unexplored task. We look into this problem through the lens of training data -
the major source of unfairness. We ask the following questions: How would the
unfairness of a model change if its training samples (1) were collected from a
different (e.g. demographic) group, (2) were labeled differently, or (3) whose
features were modified? In other words, we quantify the influence of training
samples on unfairness by counterfactually changing samples based on predefined
concepts, i.e. data attributes such as features, labels, and sensitive
attributes. Our framework not only can help practitioners understand the
observed unfairness and mitigate it by repairing their training data, but also
leads to many other applications, e.g. detecting mislabeling, fixing imbalanced
representations, and detecting fairness-targeted poisoning attacks.
