research-article

On Measuring Bias in Online Information

Authors:

Evaggelia Pitoura,

Panayiotis Tsaparas,

Giorgos Flouris,

Irini Fundulaki,

Panagiotis Papadakos,

Serge Abiteboul,

Gerhard WeikumAuthors Info & Claims

ACM SIGMOD Record, Volume 46, Issue 4

Pages 16 - 21

https://doi.org/10.1145/3186549.3186553

Published: 22 February 2018 Publication History

Abstract

Bias in online information has recently become a pressing issue, with search engines, social networks and recommendation services being accused of exhibiting some form of bias. In this vision paper, we make the case for a systematic approach towards measuring bias. To this end, we discuss formal measures for quantifying the various types of bias, we outline the system components necessary for realizing them, and we highlight the related research challenges and open problems.

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Cited By

Caton SHaas C(2024)Fairness in Machine Learning: A SurveyACM Computing Surveys10.1145/361686556:7(1-38)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3616865
Yoon SSong HShin W(2023)Impact of Quality Factors on Platform-based DecisionsJournal of Society of Korea Industrial and Systems Engineering10.11627/jksie.2023.46.3.10946:3(109-122)Online publication date: 30-Sep-2023
https://doi.org/10.11627/jksie.2023.46.3.109
Islam MAsadi MBasu Roy S(2023)Equitable Top-k Results for Long Tail DataProceedings of the ACM on Management of Data10.1145/36267271:4(1-24)Online publication date: 12-Dec-2023
https://dl.acm.org/doi/10.1145/3626727
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Information & Contributors

Information

Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 46, Issue 4

December 2017

48 pages

ISSN:0163-5808

DOI:10.1145/3186549

Editors:
Yanlei Diao
University of Massachusetts Amherst
,
Vanessa Braganholo
Universidade Federal Fluminense
,
Marco Brambilla
Politecnico di Milano
,
Chee Yong Chan
National University of Singapore
,
Rada Chirkova
North Carolina State University
,
Zackary Ives
University of Pennsylvania
,
Anastasios Kementsietsidis
Google Research
,
Jeffrey Naughton
University of Wisconsin-Madison
,
Frank Neven
Hasselt University
,
Olga Papaemmanoui
Brandeis Univesity
,
Aditya Parameswaran
University of Illinois
,
Alkis Simitsis
HP Labs
,
Wang-Chiew Tan
University of California Santa Cruz
,
Pinar Tözün
IBM Almaden Research Center
,
Marianne Winslett
University of Illinois
,
Jun Yang
Duke University

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Copyright © 2018 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 February 2018

Published in SIGMOD Volume 46, Issue 4

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Cited By

Caton SHaas C(2024)Fairness in Machine Learning: A SurveyACM Computing Surveys10.1145/361686556:7(1-38)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3616865
Yoon SSong HShin W(2023)Impact of Quality Factors on Platform-based DecisionsJournal of Society of Korea Industrial and Systems Engineering10.11627/jksie.2023.46.3.10946:3(109-122)Online publication date: 30-Sep-2023
https://doi.org/10.11627/jksie.2023.46.3.109
Islam MAsadi MBasu Roy S(2023)Equitable Top-k Results for Long Tail DataProceedings of the ACM on Management of Data10.1145/36267271:4(1-24)Online publication date: 12-Dec-2023
https://dl.acm.org/doi/10.1145/3626727
Wang BZhang RWu YAi QZhang MLiu Y(2023)Investigating the Influence of Legal Case Retrieval Systems on Users' Decision ProcessProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625321(169-175)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3624918.3625321
Sakai TKim JKang I(2023)A Versatile Framework for Evaluating Ranked Lists in Terms of Group Fairness and RelevanceACM Transactions on Information Systems10.1145/358976342:1(1-36)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3589763
Jian DBao YZhou JWu HChen HDuh WHuang HKato MMothe JPoblete B(2023)A Practical Online Allocation Framework at Industry-scale in Constrained RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591835(3270-3274)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591835
Akbar ACaton SBierig R(2023)Personalised Filter Bias with Google and DuckDuckGo: An Exploratory StudyArtificial Intelligence and Cognitive Science10.1007/978-3-031-26438-2_39(502-513)Online publication date: 23-Feb-2023
https://doi.org/10.1007/978-3-031-26438-2_39
Kuratomi APitoura EPapapetrou PLindgren TTsaparas P(2023)Measuring the Burden of (Un)fairness Using CounterfactualsMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-23618-1_27(402-417)Online publication date: 31-Jan-2023
https://doi.org/10.1007/978-3-031-23618-1_27
Xu HZhang N(2022)Implications of Data Anonymization on the Statistical Evidence of DisparityManagement Science10.1287/mnsc.2021.402868:4(2600-2618)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1287/mnsc.2021.4028
Xie TMa YKang JTong HMaciejewski R(2022)FairRankVis: A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining ModelsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.311485028:1(368-377)Online publication date: Jan-2022
https://doi.org/10.1109/TVCG.2021.3114850
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