Situation Testing-Based Discrimination Discovery: A Causal Inference Approach / 2718
Lu Zhang, Yongkai Wu, Xintao Wu

Discrimination discovery is to unveil discrimination against a specific individual by analyzing the historical dataset. In this paper, we develop a general technique to capture discrimination based on the legally grounded situation testing methodology. For any individual, we find pairs of tuples from the dataset with similar characteristics apart from belonging or not to the protected-by-law group and assign them in two groups. The individual is considered as discriminated if significant difference is observed between the decisions from the two groups. To find similar tuples, we make use of the Causal Bayesian Networks and the associated causal inference as a guideline. The causal structure of the dataset and the causal effect of each attribute on the decision are used to facilitate the similarity measurement. Through empirical assessments on a real dataset, our approach shows good efficacy both in accuracy and efficiency.

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