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

TL;DR

This paper introduces a new fairness framework for ranking systems that considers individual provider preferences over outcomes like exposure and sales, moving beyond equality-based fairness to better meet diverse provider needs.

Contribution

It proposes an equity-oriented fairness framework and develops EquityRank, a gradient-based algorithm that balances user effectiveness with provider-specific fairness objectives.

Findings

EquityRank improves trade-offs between effectiveness and fairness.

The approach adapts to heterogeneous provider needs.

Offline and online tests validate its effectiveness.

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.