  Discrimination discovery and prevention/removal are increasingly important
tasks in data mining. Discrimination discovery aims to unveil discriminatory
practices on the protected attribute (e.g., gender) by analyzing the dataset of
historical decision records, and discrimination prevention aims to remove
discrimination by modifying the biased data before conducting predictive
analysis. In this paper, we show that the key to discrimination discovery and
prevention is to find the meaningful partitions that can be used to provide
quantitative evidences for the judgment of discrimination. With the support of
the causal graph, we present a graphical condition for identifying a meaningful
partition. Based on that, we develop a simple criterion for the claim of
non-discrimination, and propose discrimination removal algorithms which
accurately remove discrimination while retaining good data utility. Experiments
using real datasets show the effectiveness of our approaches.
