Anti-discrimination learning: a causal modeling-based framework

Anti-discrimination learning is an increasingly important task in data mining. Discrimination discovery is the problem of unveiling discriminatory practices by analyzing a dataset of historical decision records, and discrimination prevention aims to remove discrimination by modifying the biased data and/or the predictive algorithms. Discrimination is causal, which means that to prove discrimination one needs to derive a causal relationship rather than an association relationship. Although it is well known that association does not mean causation, the gap between association and causation is not paid enough attention by many researchers. In this paper, we introduce a causal modeling-based framework for anti-discrimination learning. Discrimination is categorized according to two dimensions: direct/indirect and system/group/individual level. Within the causal framework, we introduce a work for discovering and preventing both direct and indirect system-level discrimination in the training data, and a work for extending the non-discrimination result from the training data to prediction. We then introduce two works for group-level direct discrimination and individual-level direct discrimination respectively. The aim of this paper is to deepen the understanding of discrimination in data mining from the causal modeling perspective, and suggest several potential future research directions.