Contrastive Counterfactual Fairness in Algorithmic Decision-Making

Abstract

The widespread use of artificial intelligence algorithms and their role in decision-making with consequential decisions for human subjects has resulted in a growing interest in designing AI algorithms accounting for fairness considerations. There have been attempts to account for fairness of AI algorithms without compromising their accuracy to improve poorly designed algorithms that disregard sensitive attributes (e.g., age, race, and gender) at the peril of introducing or increasing bias against specific groups. Although many studies have examined the optimal trade-off between fairness and accuracy, it remains a challenge to understand the sources of unfairness in decision-making and mitigate it effectively. To tackle this problem, researchers have proposed fair causal learning approaches which assist us in modeling cause and effect knowledge structures, discovering bias sources, and refining AI algorithms to make them more transparent and explainable. In this study, we formalize probabilistic interpretations of both contrastive and counterfactual causality as essential features in order to encourage users' trust and to expand the applicability of such automated systems. We use this formalism to define a novel fairness criterion that we call contrastive counterfactual fairness. This paper introduces, to the best of our knowledge, the first probabilistic fairness-Aware data augmentation approach that is based on contrastive counterfactual causality. We tested our approach on two well-known fairness-related datasets, UCI Adult and German Credit, and concluded that our proposed method has a promising ability to capture and mitigate unfairness in AI deployment. This model-Agnostic approach can be used with any AI model because it is applied in pre-processing.