Reducing disparate exposure in ranking: A learning to rank approach
Open accessAbstract
Ranked search results have become the main mechanism by which we find content, products, places, and people online. Thus their ordering contributes not only to the satisfaction of the searcher, but also to career and business opportunities, educational placement, and even social success of those being ranked. Researchers have become increasingly concerned with systematic biases in data-driven ranking models, and various post-processing methods have been proposed to mitigate discrimination and inequality of opportunity. This approach, however, has the disadvantage that it still allows an unfair ranking model to be trained.
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Fairness in a recommender system can be evaluated through the ranking of the recommended items list, the prediction accuracy of the recommendation, and pairwise metrics.
“Fairness in an RS can be evaluated through the ranking of the recommended items list [50], the prediction accuracy of the recommendation [4, 31, 51], and pairwise metrics [52].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [50]
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Accurately interpreting clickthrough data as implicit feedback
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BackgroundA Nutritional Label for Rankings
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“ird, there can be systematic dierences in the way in which documents are constructed, as in the case of dierent sections in online resumes, which are completed at dierent rates by men and women [2]; these dierences may systematically aect ranking algorithms.”
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Cited by
Sorted by citations
A bias detection tree approach for detecting disparities in a recommendation model’s errors
Misztal-Radecka, J., & Indurkhya, B. (2023) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-022-09334-x · Cited by 4 · see citing works
Roy, F., Ding, X., Choo, K. K. R., & Zhou, P. (2024) · arXiv · doi:10.48550/arxiv.2409.12651
- Authors
- Zehlike, M., & Castillo, C.
- Affiliations
- Humboldt-Universität zu Berlin (DE)
- Association for Computing Machinery (US)
- Venue
- Proceedings of The Web Conference 2020
- Publisher
- ACM
- Published
- 2020-04-20
- Pages
- 2849-2855
- OpenAlex
- W2804585190
- Cited by
- 159
- References
- 4
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 30 · 2023: 23 · 2022: 32 · 2021: 36 · 2020: 10 · 2019: 4
- FWCI
- 26.19
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Max Planck Institute for Software Systems
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- learning to rank, ranking bias, algorithmic discrimination, inequality of opportunity, post-processing, search result ranking
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