tutorial

Fairness of Machine Learning in Search Engines

Authors:

Mikhail YurochkinAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 5132 - 5135

https://doi.org/10.1145/3511808.3557501

Published: 17 October 2022 Publication History

Abstract

Fairness has gained increasing importance in a variety of AI and machine learning contexts. As one of the most ubiquitous applications of machine learning, search engines mediate much of the information experiences of members of society. Consequently, understanding and mitigating potential algorithmic unfairness in search have become crucial for both users and systems. In this tutorial, we will introduce the fundamentals of fairness in machine learning, for both supervised learning such as classification and ranking, and unsupervised learning such as clustering. We will then present the existing work on fairness in search engines, including the fairness definitions, evaluation metrics, and taxonomies of methodologies. This tutorial will help orient information retrieval researchers to algorithmic fairness, provide an introduction to the growing literature on this topic, and gathering researchers and practitioners interested in this research direction.

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Cited By

Zhang FChen CHua XLuo X(2024)FATE: Learning Effective Binary Descriptors With Group FairnessIEEE Transactions on Image Processing10.1109/TIP.2024.340613433(3648-3661)Online publication date: 2024
https://doi.org/10.1109/TIP.2024.3406134
Zeng HYue ZJiang QZhang YShang LZong RWang D(2024)Mitigating Demographic Bias of Federated Learning Models via Robust-Fair Domain Smoothing: A Domain-Shifting Approach2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS60910.2024.00078(785-796)Online publication date: 23-Jul-2024
https://doi.org/10.1109/ICDCS60910.2024.00078
Sonoda R(2023)Fair oversampling technique using heterogeneous clustersInformation Sciences: an International Journal10.1016/j.ins.2023.119059640:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ins.2023.119059

Index Terms

Fairness of Machine Learning in Search Engines
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 ACM.

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Published: 17 October 2022

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CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Zhang FChen CHua XLuo X(2024)FATE: Learning Effective Binary Descriptors With Group FairnessIEEE Transactions on Image Processing10.1109/TIP.2024.340613433(3648-3661)Online publication date: 2024
https://doi.org/10.1109/TIP.2024.3406134
Zeng HYue ZJiang QZhang YShang LZong RWang D(2024)Mitigating Demographic Bias of Federated Learning Models via Robust-Fair Domain Smoothing: A Domain-Shifting Approach2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS60910.2024.00078(785-796)Online publication date: 23-Jul-2024
https://doi.org/10.1109/ICDCS60910.2024.00078
Sonoda R(2023)Fair oversampling technique using heterogeneous clustersInformation Sciences: an International Journal10.1016/j.ins.2023.119059640:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ins.2023.119059

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