Strategic Costs of Perceived Bias in Fair Selection
L. Elisa Celis, Lingxiao Huang, Milind Sohoni, Nisheeth K. Vishnoi
Meritocratic systems, from admissions to hiring, aim to impartially reward skill and effort. Yet, observed disparities in outcomes across race, gender, or class challenge this ideal. Some attribute these gaps to structural inequality; others to individual choice. We introduce a game-theoretic model in which candidates from different socioeconomic groups differ in perceived post-selection value; such values are shaped by social context and (increasingly) by AI-powered tools that can provide personalized career or salary guidance. In our model, each candidate strategically chooses the amount of effort to expend, weighing the cost against the expected reward. Effort is converted into observable merit, and selection is based solely on merit. We characterize the unique Nash equilibrium in the large-agent limit and derive explicit expressions for how valuation disparities and institutional selectivity jointly shape effort, representation, social welfare, and utility. We also formulate a cost-sensitive optimization framework to guide interventions by quantifying how shifts in selectivity or perceived value can reduce disparities without sacrificing institutional goals. Our results uncover a behavioral feedback loop: algorithmic or social signals about post-selection value distort pre-selection effort, propagating disparities backward through otherwise ``fair'' processes. This reconciles competing explanations of inequality—rational choice vs. structural bias—by showing how techno-social environments shape individual incentives in meritocratic systems.