Strategic Costs of Perceived Bias in Fair Selection

2025-10-23

AI Summary

This paper investigates how socioeconomic background disparities induce intergroup “perceived-value bias” in formally fair merit-based selection, leading to rational divergence in effort investment and thereby reproducing and exacerbating inequalities in representation, social welfare, and utility. Method: We introduce the novel concept of *perceived-value bias* and develop a game-theoretic model in the large-population mean-field limit, deriving closed-form analytical expressions for equilibrium effort levels, group representation, and social welfare under Nash equilibrium. Contribution/Results: Even under strictly meritocratic selection, interventions targeting perceived value—e.g., through information provision or modest relaxation of selection thresholds—significantly reduce inequality across all dimensions without compromising institutional efficacy. The framework bridges structural inequity and individual rational choice, offering a quantifiable, cost-sensitive foundation for fairness-aware policy design.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

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 outcome--bridging rational-choice and structural explanations of inequality by showing how techno-social environments shape individual incentives in meritocratic systems.

Problem

• Modeling how perceived post-selection value disparities affect merit-based selection outcomes
• Analyzing how valuation gaps rationalize effort differences in fair systems
• Quantifying interventions to reduce disparities without compromising institutional goals

Innovation

• Game-theoretic model analyzes perceived post-selection value disparities
• Cost-sensitive optimization reduces disparities without compromising goals
• Quantifies how techno-social environments shape meritocratic incentives