Addressing Strategic Manipulation Disparities in Fair Classification
This addresses fairness issues in strategic settings like loan approvals, where minority groups face higher costs to manipulate features, though it is incremental as it builds on existing fair classification methods.
The paper tackles the problem of strategic manipulation cost disparities in fair classification, showing that standard fairness constraints fail to reduce these costs for minority groups, and proposes a constrained optimization framework that lowers strategic manipulation costs for minorities, with empirical validation on real-world datasets.
In real-world classification settings, such as loan application evaluation or content moderation on online platforms, individuals respond to classifier predictions by strategically updating their features to increase their likelihood of receiving a particular (positive) decision (at a certain cost). Yet, when different demographic groups have different feature distributions or pay different update costs, prior work has shown that individuals from minority groups often pay a higher cost to update their features. Fair classification aims to address such classifier performance disparities by constraining the classifiers to satisfy statistical fairness properties. However, we show that standard fairness constraints do not guarantee that the constrained classifier reduces the disparity in strategic manipulation cost. To address such biases in strategic settings and provide equal opportunities for strategic manipulation, we propose a constrained optimization framework that constructs classifiers that lower the strategic manipulation cost for minority groups. We develop our framework by studying theoretical connections between group-specific strategic cost disparity and standard selection rate fairness metrics (e.g., statistical rate and true positive rate). Empirically, we show the efficacy of this approach over multiple real-world datasets.