  The discovery of discriminatory bias in human or automated decision making is
a task of increasing importance and difficulty, exacerbated by the pervasive
use of machine learning and data mining. Currently, discrimination discovery
largely relies upon correlation analysis of decisions records, disregarding the
impact of confounding biases. We present a method for causal discrimination
discovery based on propensity score analysis, a statistical tool for filtering
out the effect of confounding variables. We introduce causal measures of
discrimination which quantify the effect of group membership on the decisions,
and highlight causal discrimination/favoritism patterns by learning regression
trees over the novel measures. We validate our approach on two real world
datasets. Our proposed framework for causal discrimination has the potential to
enhance the transparency of machine learning with tools for detecting
discriminatory bias both in the training data and in the learning algorithms.
