Strategic Costs of Perceived Bias in Fair Selection

L. Elisa Celis, Lingxiao Huang, Milind Sohoni, Nisheeth K. Vishnoi

Keywords: Meritocratic selection, Strategic effort, Game-theoretic modeling, Perceived valuation bias, Algorithmic fairness, Social feedback loops, Representation and efficiency, Nash equilibrium, Human-AI interaction

TL;DR: We show how perceived post-selection bias distorts strategic effort in merit-based selection, leading to disparities. Our model quantifies interventions to reduce inequity by adjusting selectivity and perceived valuation gaps.

Abstract: Meritocratic systems, from admissions to hiring, aim to impartially reward skill and effort. Yet persistent disparities across race, gender, and class challenge this ideal. Some attribute these gaps to structural inequality; others to individual choice. We develop a game-theoretic model in which candidates from different socioeconomic groups differ in their perceived post-selection value—shaped by social context and, increasingly, by AI-powered tools offering personalized career or salary guidance. Each candidate strategically chooses effort, balancing its cost against expected reward; effort translates into observable merit, and selection is based solely on merit. We characterize the unique Nash equilibrium in the large-agent limit and derive explicit formulas showing how valuation disparities and institutional selectivity jointly determine effort, representation, social welfare, and utility. We further propose a cost-sensitive optimization framework that quantifies how modifying selectivity or perceived value can reduce disparities without compromising institutional goals. Our analysis reveals a perception-driven bias: when perceptions of post-selection value differ across groups, these differences translate into rational differences in effort, propagating disparities backward through otherwise "fair" selection processes. While the model is static, it captures one stage of a broader feedback cycle linking perceptions, incentives, and outcomes—bridging rational-choice and structural explanations of inequality by showing how techno-social environments shape individual incentives in meritocratic systems.

Primary Area: Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Submission Number: 7381