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Fairness and discrimination in recommendation and retrieval

Authors:

Michael D Ekstrand,

Fernando DiazAuthors Info & Claims

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

Pages 576 - 577

https://doi.org/10.1145/3298689.3346964

Published: 10 September 2019 Publication History

Abstract

Fairness and related concerns have become of increasing importance in a variety of AI and machine learning contexts. They are also highly relevant to recommender systems and related problems such as information retrieval, as evidenced by the growing literature in RecSys, FAT*, SIGIR, and special sessions such as the FATREC and FACTS-IR workshops and the Fairness track at TREC 2019; however, translating algorithmic fairness constructs from classification, scoring, and even many ranking settings into recommendation and other information access scenarios is not a straightforward task. This tutorial will help orient RecSys researchers to algorithmic fairness, understand how concepts do and do not translate from other settings, and provide an introduction to the growing literature on this topic.

References

[1]

N. J. Belkin and S. E. Robertson. 1976. Some ethical and political implications of theoretical research in information science. In Proceedings of the ASIS Annual Meeting.

[2]

Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, and Ed H. Chi. 2019. Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements. CoRR abs/1901.04562 (2019).

[3]

Asia J Biega, Krishna P Gummadi, and Gerhard Weikum. 2018. Equity of Attention: Amortizing Individual Fairness in Rankings. In Proc. SIGIR '18. ACM, 405--414.

Digital Library

[4]

Robin Burke. 2017. Multisided Fairness for Recommendation. (July 2017). arXiv:cs.CY/1707.00093 http://arxiv.org/abs/1707.00093

[5]

Alexandra Chouldechova and Aaron Roth. 2018. The Frontiers of Fairness in Machine Learning. (Oct. 2018). arXiv:cs.LG/1810.08810 http://arxiv.org/abs/1810.08810

[6]

Fernando Diaz. 2016. Worst Practices for Designing Production Information Access Systems. SIGIR Forum 50, 1 (June 2016), 2--11.

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[7]

Michael D Ekstrand and Amit Sharma. 2017. FATREC Workshop on Responsible Recommendation. In Proc. ACM RecSys '18. ACM, 382--383.

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[8]

Michael D Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D Ekstrand, Oghenemaro Anuyah, David McNeill, Pera, and Maria Soledad. 2018. All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. In Proceedings of the Conference on Fairness, Accountability, and Transparency (PMLR), Vol. 81. 172âĂŞ186. http://proceedings.mlr.press/v81/ekstrand18b.html

[9]

Michael D Ekstrand, Mucun Tian, Mohammed R Imran Kazi, Hoda Mehrpouyan, and Daniel Kluver. 2018. Exploring Author Gender in Book Rating and Recommendation. In Proc. ACM RecSys '18. ACM.

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Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudík, and Hanna Wallach. 2019. Improving fairness in machine learning systems: What do industry practitioners need?. In Proc. CHI 2019.

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Toshihiro Kamishima, Pierre-Nicolas Schwab, and Michael D Ekstrand. 2018. 2nd FATREC workshop: responsible recommendation. In Proc. ACM RecSys '18. ACM, 516--516.

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[12]

Rishabh Mehrotra, Ashton Anderson, Fernando Diaz, Amit Sharma, Hanna Wallach, and Emine Yilmaz. 2017. Auditing Search Engines for Differential Satisfaction Across Demographics. In WWW '17 Companion. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 626--633.

Digital Library

[13]

Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, and Fernando Diaz. 2018. Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness and Satisfaction in Recommendation Systems. In Proc. CIKM '18.

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Safiya Umoja Noble. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.

[15]

Ashudeep Singh and Thorsten Joachims. 2018. Fairness of Exposure in Rankings. In Proc. KDD '18 (KDD '18). ACM, New York, NY, USA, 2219--2228.

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Cited By

Mikhaylovskaya A(2024)Enhancing Deliberation with Digital Democratic InnovationsPhilosophy & Technology10.1007/s13347-023-00692-x37:1Online publication date: 4-Jan-2024
https://doi.org/10.1007/s13347-023-00692-x
Pipergias Analytis PHager P(2023)Collaborative filtering algorithms are prone to mainstream-taste biasProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608825(750-756)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608825
Stoyanovich JZehlike MYang KDas SPandis ISelçuk Candan KAmer-Yahia S(2023)Fairness in Ranking: From Values to Technical Choices and BackCompanion of the 2023 International Conference on Management of Data10.1145/3555041.3589405(7-12)Online publication date: 4-Jun-2023
https://dl.acm.org/doi/10.1145/3555041.3589405
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Index Terms

Fairness and discrimination in recommendation and retrieval
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
2. Social and professional topics
  1. User characteristics

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Information & Contributors

Information

Published In

cover image ACM Other conferences

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

September 2019

635 pages

ISBN:9781450362436

DOI:10.1145/3298689

General Chairs:
Toine Bogers
Aalborg University Copenhagen, Denmark
,
Alan Said
University of Gothenburg, Sweden
,
Program Chairs:
Peter Brusilovsky
University of Pittsburgh
,
Domonkos Tikk
Gravity R&D, Hungary

Copyright © 2019 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 September 2019

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Tutorial

Funding Sources

National Science Foundation

Conference

RecSys '19

RecSys '19: Thirteenth ACM Conference on Recommender Systems

September 16 - 20, 2019

Copenhagen, Denmark

Acceptance Rates

RecSys '19 Paper Acceptance Rate 36 of 189 submissions, 19%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Citations

Cited By

Mikhaylovskaya A(2024)Enhancing Deliberation with Digital Democratic InnovationsPhilosophy & Technology10.1007/s13347-023-00692-x37:1Online publication date: 4-Jan-2024
https://doi.org/10.1007/s13347-023-00692-x
Pipergias Analytis PHager P(2023)Collaborative filtering algorithms are prone to mainstream-taste biasProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608825(750-756)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608825
Stoyanovich JZehlike MYang KDas SPandis ISelçuk Candan KAmer-Yahia S(2023)Fairness in Ranking: From Values to Technical Choices and BackCompanion of the 2023 International Conference on Management of Data10.1145/3555041.3589405(7-12)Online publication date: 4-Jun-2023
https://dl.acm.org/doi/10.1145/3555041.3589405
Yang MWang JTon JChen HDuh WHuang HKato MMothe JPoblete B(2023)Rectifying Unfairness in Recommendation Feedback LoopProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591754(28-37)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591754
Dinnissen KBauer C(2022)Fairness in Music Recommender Systems: A Stakeholder-Centered Mini ReviewFrontiers in Big Data10.3389/fdata.2022.9136085Online publication date: 22-Jul-2022
https://doi.org/10.3389/fdata.2022.913608
Ghajargar MBardzell J(2022)Making AI Understandable by Making it Tangible: Exploring the Design Space with Ten Concept CardsProceedings of the 34th Australian Conference on Human-Computer Interaction10.1145/3572921.3572942(74-80)Online publication date: 29-Nov-2022
https://dl.acm.org/doi/10.1145/3572921.3572942
Ghajargar MBardzell J(2022)Learning About Plant Intelligence from a Flying Plum Tree: Music Recommenders and Posthuman User ExperiencesProceedings of the 25th International Academic Mindtrek Conference10.1145/3569219.3569388(343-346)Online publication date: 16-Nov-2022
https://dl.acm.org/doi/10.1145/3569219.3569388
Zangerle EBauer C(2022)Evaluating Recommender Systems: Survey and FrameworkACM Computing Surveys10.1145/355653655:8(1-38)Online publication date: 23-Dec-2022
https://dl.acm.org/doi/10.1145/3556536
Zehlike MYang KStoyanovich J(2022)Fairness in Ranking, Part I: Score-Based RankingACM Computing Surveys10.1145/353337955:6(1-36)Online publication date: 7-Dec-2022
https://dl.acm.org/doi/10.1145/3533379
Brown TLiu JAizawa AMandl TCarevic ZHinze AMayr PSchaer P(2022)A reference dependence approach to enhancing early prediction of session behavior and satisfactionProceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries10.1145/3529372.3533294(1-5)Online publication date: 20-Jun-2022
https://dl.acm.org/doi/10.1145/3529372.3533294
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