We present contrastive fairness, a new direction in causal inference applied to algorithmic fairness and argue that just asking the traditional counterfactual "what if?" question might not be enough. 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 (eg. 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. This work focuses on laying down the overarching theoretical and mathematical aspects with illustrative thought examples, with the aim of deploying them to solve relevant applications in future.
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