Individual Fairness In Strategic Classification
This addresses fairness challenges for individuals in strategic classification settings, offering a novel approach to an underexplored area.
The paper tackles the problem of individual fairness in strategic classification, where deterministic thresholds violate fairness, and proposes a randomized classifier that ensures individual fairness under certain conditions, with experiments on real-world datasets showing effective mitigation of unfairness and improved fairness-accuracy trade-off.
Strategic classification, where individuals modify their features to influence machine learning (ML) decisions, presents critical fairness challenges. While group fairness in this setting has been widely studied, individual fairness remains underexplored. We analyze threshold-based classifiers and prove that deterministic thresholds violate individual fairness. Then, we investigate the possibility of using a randomized classifier to achieve individual fairness. We introduce conditions under which a randomized classifier ensures individual fairness and leverage these conditions to find an optimal and individually fair randomized classifier through a linear programming problem. Additionally, we demonstrate that our approach can be extended to group fairness notions. Experiments on real-world datasets confirm that our method effectively mitigates unfairness and improves the fairness-accuracy trade-off.