Counterfactual Fairness and Utility Trade-offs: Characterizing the Pareto Frontier

Artificial intelligence is increasingly utilized in high-stakes decision-making applications, and this trend is expected to continue growing, resulting in the fairness concerns becoming increasingly significant. While achieving fairness is an idealized goal, it often comes at a cost. From a machine learning perspective, fairness constraints can increase the prediction error and reduce the predictive utility of a model. Such losses in predictive accuracy may also harm individuals or society in some real-world applications. Therefore, it is necessary to consider the balance between fairness and utility by allowing some degree of fairness relaxation in such settings. The main challenge lies in how to achieve optimal trade-offs and how to define the trade-offs between fairness and utility. We study this problem under the framework of counterfactual fairness (CF). We formulate a quantifiable and interpretable measurement of fairness relaxation and characterize the Pareto-optimal predictor at each fairness level. With incomplete causal knowledge, we provide a theoretical analysis of the decomposition and variation of the excess risk bound. Synthetic experiments validate our theoretical results and semi-synthetic experiments based on real-world datasets further demonstrate the effectiveness of our method in more practical settings.