A Taxation Perspective for Fair Re-ranking

TL;DR

This paper introduces Tax-rank, a novel fair re-ranking method inspired by taxation principles, which improves fairness and controllability in ranking systems through optimal transport, outperforming existing methods in recommendation and advertising tasks.

Contribution

The paper reformulates fair re-ranking as a taxation problem, identifies limitations of previous tax policies, and proposes Tax-rank with superior theoretical properties and empirical performance.

Findings

Tax-rank achieves better fairness and accuracy trade-offs.

It demonstrates superior efficiency over baseline methods.

Theoretical analysis confirms continuity and controllability of the proposed method.

Abstract

Fair re-ranking aims to redistribute ranking slots among items more equitably to ensure responsibility and ethics. The exploration of redistribution problems has a long history in economics, offering valuable insights for conceptualizing fair re-ranking as a taxation process. Such a formulation provides us with a fresh perspective to re-examine fair re-ranking and inspire the development of new methods. From a taxation perspective, we theoretically demonstrate that most previous fair re-ranking methods can be reformulated as an item-level tax policy. Ideally, a good tax policy should be effective and conveniently controllable to adjust ranking resources. However, both empirical and theoretical analyses indicate that the previous item-level tax policy cannot meet two ideal controllable requirements: (1) continuity, ensuring minor changes in tax rates result in small accuracy and fairness shifts; (2) controllability over accuracy loss, ensuring precise estimation of the accuracy loss under a specific tax rate. To overcome these challenges, we introduce a new fair re-ranking method named Tax-rank, which levies taxes based on the difference in utility between two items. Then, we efficiently optimize such an objective by utilizing the Sinkhorn algorithm in optimal transport. Upon a comprehensive analysis, Our model Tax-rank offers a superior tax policy for fair re-ranking, theoretically demonstrating both continuity and controllability over accuracy loss. Experimental results show that Tax-rank outperforms all state-of-the-art baselines in terms of effectiveness and efficiency on recommendation and advertising tasks.