  The popularity of machine learning has increased the risk of unfair models
getting deployed in high-stake applications, such as justice system,
drug/vaccination design, and medical diagnosis. Although there are effective
methods to train fair models from scratch, how to automatically reveal and
explain the unfairness of a trained model remains a challenging task. Revealing
unfairness of machine learning models in interpretable fashion is a critical
step towards fair and trustworthy AI. In this paper, we systematically tackle
the novel task of revealing unfair models by mining interpretable evidence
(RUMIE). The key idea is to find solid evidence in the form of a group of data
instances discriminated most by the model. To make the evidence interpretable,
we also find a set of human-understandable key attributes and decision rules
that characterize the discriminated data instances and distinguish them from
the other non-discriminated data. As demonstrated by extensive experiments on
many real-world data sets, our method finds highly interpretable and solid
evidence to effectively reveal the unfairness of trained models. Moreover, it
is much more scalable than all of the baseline methods.
