Achieving non-discrimination in data release
This work addresses discrimination in data release for data mining applications, presenting an incremental improvement with a graphical condition and algorithms.
The paper tackled the problem of discrimination in data mining by developing a method to identify meaningful partitions for discrimination discovery and removal, achieving effective discrimination removal while maintaining good data utility in experiments with real datasets.
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.