Actionable Recourse in Competitive Environments: A Dynamic Game of Endogenous Selection
This addresses the issue of fairness in AI-assisted decision-making for individuals in competitive settings, revealing that recourse can exacerbate inequalities rather than mitigate them.
The study tackled the problem of actionable recourse in competitive environments like admissions, where multiple candidates can strategically improve their features, and found that this leads to endogenous selection that amplifies initial disparities and creates persistent performance gaps.
Actionable recourse studies whether individuals can modify feasible features to overturn unfavorable outcomes produced by AI-assisted decision-support systems. However, many such systems operate in competitive settings, such as admission or hiring, where only a fraction of candidates can succeed. A fundamental question arises: what happens when actionable recourse is available to everyone in a competitive environment? This study proposes a framework that models recourse as a strategic interaction among candidates under a risk-based selection rule. Rejected individuals exert effort to improve actionable features along directions implied by the decision rule, while the success benchmark evolves endogenously as many candidates adjust simultaneously. This creates endogenous selection, in which both the decision rule and the selection threshold are determined by the population's current feature state. This interaction generates a closed-loop dynamical system linking candidate selection and strategic recourse. We show that the initially selected candidates determine both the benchmark of success and the direction of improvement, thereby amplifying initial disparities and producing persistent performance gaps across the population.