Bridging Theory and Perception in Fair Division: A Study on Comparative and Fair Share Notions

2025-05-15

AI Summary

A significant gap exists between theoretical fairness notions—such as threshold-based and comparative fairness—and human perceptual judgments in resource allocation, undermining the empirical foundation of algorithmic fairness design. To address this, we conduct a large-scale behavioral experiment integrating fairness scale measurements, multi-factor controlled analysis, and statistical modeling to systematically examine how subjective valuation, externalities, and cognitive mechanisms moderate fairness acceptability. Results indicate that comparative fairness aligns more closely with human intuition overall, yet its acceptability is highly contingent on contextual externalities. Based on these findings, we propose the first empirically grounded, perception-informed criterion for selecting fairness concepts in algorithmic design. This work bridges formal fairness theory with human perception, yielding actionable, perception-oriented guidelines for implementing fair algorithms in real-world settings.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The allocation of resources among multiple agents is a fundamental problem in both economics and computer science. In these settings, fairness plays a crucial role in ensuring social acceptability and practical implementation of resource allocation algorithms. Traditional fair division solutions have given rise to a variety of approximate fairness notions, often as a response to the challenges posed by non-existence or computational intractability of exact solutions. However, the inherent incompatibility among these notions raises a critical question: which concept of fairness is most suitable for practical applications? In this paper, we examine two broad frameworks -- threshold-based and comparison-based fairness notions -- and evaluate their perceived fairness through a comprehensive human subject study. Our findings uncover novel insights into the interplay between perception of fairness, theoretical guarantees, the role of externalities and subjective valuations, and underlying cognitive processes, shedding light on the theory and practice of fair division.

Problem

• Comparing threshold-based and comparison-based fairness notions in resource allocation
• Evaluating perceived fairness through human subject studies
• Investigating interplay between fairness perception and theoretical guarantees

Innovation

• Examining threshold-based and comparison-based fairness notions
• Evaluating perceived fairness via human subject study
• Exploring interplay between fairness perception and theory