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Anti-discrimination is an increasingly important task in data science. Inthis paper, we investigate the problem of discovering both direct and indirectdiscrimination from the historical data, and removing the discriminatoryeffects before the data is used for predictive analysis (e.g., buildingclassifiers). We make use of the causal network to capture the causal structureof the data. Then we model direct and indirect discrimination as thepath-specific effects, which explicitly distinguish the two types ofdiscrimination as the causal effects transmitted along different paths in thenetwork. Based on that, we propose an effective algorithm for discoveringdirect and indirect discrimination, as well as an algorithm for preciselyremoving both types of discrimination while retaining good data utility.Different from previous works, our approaches can ensure that the predictivemodels built from the modified data will not incur discrimination in decisionmaking. Experiments using real datasets show the effectiveness of ourapproaches.

A causal framework for discovering and removing direct and indirect discrimination