Contrastive Fairness in Machine Learning
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We present contrastive fairness, a new direction in causal inference applied to algorithmic fairness. Earlier methods dealt with the "what if?" question (counterfactual fairness, NeurIPS'17). We establish the theoretical and mathematical implications of the contrastive question "why this and not that?" in context of algorithmic fairness in machine learning. This is essential to defend the fairness of algorithmic decisions in tasks where a person or sub-group of people is chosen over another (job recruitment, university admission, company layovers, etc). This development is also helpful to institutions to ensure or defend the fairness of their automated decision making processes. A test case of employee job location allocation is provided as an illustrative example.
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