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A Taxation Perspective for Fair Re-ranking

Authors:

Tat-Seng ChuaAuthors Info & Claims

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1494 - 1503

https://doi.org/10.1145/3626772.3657766

Published: 11 July 2024 Publication History

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 on two ranking tasks.

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Cited By

Ye XXu CXu JXie XWang GDong ZSerra ESpezzano F(2024)Guaranteeing Accuracy and Fairness under Fluctuating User Traffic: A Bankruptcy-Inspired Re-ranking ApproachProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679590(2991-3001)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679590

Index Terms

A Taxation Perspective for Fair Re-ranking
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2024

3164 pages

ISBN:9798400704314

DOI:10.1145/3626772

General Chairs:
Grace Hui Yang
Georgetown University, USA
,
Hongning Wang
Tsinghua University, China
,
Sam Han
The Washington Post, USA
,
Program Chairs:
Claudia Hauff
Spotify, Netherlands
,
Guido Zuccon
The University of Queensland, Australia
,
Yi Zhang
University of California Santa Cruz, USA

Copyright © 2024 ACM.

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Published: 11 July 2024

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Youth Innovation Promotion Association CAS
National Key R\&D Program of China
National Natural Science Foundation of China

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SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 14 - 18, 2024

Washington DC, USA

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Cited By

Ye XXu CXu JXie XWang GDong ZSerra ESpezzano F(2024)Guaranteeing Accuracy and Fairness under Fluctuating User Traffic: A Bankruptcy-Inspired Re-ranking ApproachProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679590(2991-3001)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679590

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