Equity vs. Equality: Optimizing Ranking Fairness for Tailored Provider Needs

2026-01-31

AI Summary

This work proposes EquityRank, a novel equity-oriented ranking framework that addresses the limitations of conventional fairness-aware ranking systems, which typically enforce equality-based fairness and overlook content providers’ heterogeneous utility preferences over outcomes such as exposure and sales. Departing from egalitarian principles, EquityRank introduces a personalized notion of fairness by explicitly modeling providers’ individualized preferences across multiple outcome dimensions. The framework jointly optimizes user relevance and provider-side equity objectives through gradient-based learning, enabling a shift from “equal treatment” to “fairness according to need.” Extensive offline evaluations and online simulations demonstrate that EquityRank significantly enhances provider-level fairness while maintaining user experience, effectively accommodating diverse provider requirements and achieving a superior trade-off between effectiveness and equity.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Ranking plays a central role in connecting users and providers in Information Retrieval (IR) systems, making provider-side fairness an important challenge. While recent research has begun to address fairness in ranking, most existing approaches adopt an equality-based perspective, aiming to ensure that providers with similar content receive similar exposure. However, it overlooks the diverse needs of real-world providers, whose utility from ranking may depend not only on exposure but also on outcomes like sales or engagement. Consequently, exposure-based fairness may not accurately capture the true utility perceived by different providers with varying priorities. To this end, we introduce an equity-oriented fairness framework that explicitly models each provider's preferences over key outcomes such as exposure and sales, thus evaluating whether a ranking algorithm can fulfill these individualized goals while maintaining overall fairness across providers. Based on this framework, we develop EquityRank, a gradient-based algorithm that jointly optimizes user-side effectiveness and provider-side equity. Extensive offline and online simulations demonstrate that EquityRank offers improved trade-offs between effectiveness and fairness and adapts to heterogeneous provider needs.

Problem

• ranking fairness
• provider-side fairness
• equity
• exposure
• utility

Innovation

• equity-oriented fairness
• provider-side fairness
• personalized utility
• gradient-based ranking optimization
• EquityRank