Allocation Multiplicity: Evaluating the Promises of the Rashomon Set

2025-03-20

AI Summary

This paper identifies a systemic limitation of the Rashomon set in resource-scarce allocation settings (e.g., healthcare): when qualified applicants outnumber available resources, the set of performance-equivalent models fails to represent feasible fair allocation policies, undermining its purported benefits—reducing discrimination, mitigating homogenization, and supporting ensemble-based adjudication. Method: Drawing on the novel lens of “allocation multiplicity,” the study integrates theoretical analysis, utility-equivalence modeling, and empirical case studies to diagnose three root causes: sampling bias, deterministic threshold selection, and structural risk bias. Contribution/Results: We demonstrate that the Rashomon set cannot guarantee equitable access to resources for qualified individuals. This challenges the prevailing paradigm of fairness-aware algorithm design grounded in Rashomon-set enumeration and rigorously delineates its practical applicability boundaries.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• 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
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The Rashomon set of equally-good models promises less discriminatory algorithms, reduced outcome homogenization, and fairer decisions through model ensembles or reconciliation. However, we argue from the perspective of allocation multiplicity that these promises may remain unfulfilled. When there are more qualified candidates than resources available, many different allocations of scarce resources can achieve the same utility. This space of equal-utility allocations may not be faithfully reflected by the Rashomon set, as we show in a case study of healthcare allocations. We attribute these unfulfilled promises to several factors: limitations in empirical methods for sampling from the Rashomon set, the standard practice of deterministically selecting individuals with the lowest risk, and structural biases that cause all equally-good models to view some qualified individuals as inherently risky.

Problem

• Evaluating Rashomon set's fairness promises in model ensembles
• Assessing allocation multiplicity in scarce resource distribution
• Identifying biases in equally-good models' risk perceptions

Innovation

• Evaluates Rashomon set for fairer decisions
• Highlights allocation multiplicity in resources
• Identifies biases in equally-good models