  Algorithmic fairness has aroused considerable interests in data mining and
machine learning communities recently. So far the existing research has been
mostly focusing on the development of quantitative metrics to measure algorithm
disparities across different protected groups, and approaches for adjusting the
algorithm output to reduce such disparities. In this paper, we propose to study
the problem of identification of the source of model disparities. Unlike
existing interpretation methods which typically learn feature importance, we
consider the causal relationships among feature variables and propose a novel
framework to decompose the disparity into the sum of contributions from
fairness-aware causal paths, which are paths linking the sensitive attribute
and the final predictions, on the graph. We also consider the scenario when the
directions on certain edges within those paths cannot be determined. Our
framework is also model agnostic and applicable to a variety of quantitative
disparity measures. Empirical evaluations on both synthetic and real-world data
sets are provided to show that our method can provide precise and comprehensive
explanations to the model disparities.
