Fairness metrics and bias mitigation strategies for rating predictions
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ML Fairness Gym is part of Google's Open AI project, a simulation toolkit to study long-run impacts of ML decisions, and analyzes how algorithms that take fairness into consideration change the underlying data (previous classifications) over time.
“ML Fairness Gym Part of Google's Open AI project, a simulation toolkit to study long-run impacts of ML decisions.5 Analyzes how algorithms that take fairness into consideration change the underlying data (previous classiications) over time (see e.g.[4,5,8,10,12]).”
From Fairness in machine learning: A survey (2024) · cites this paper as [12]
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Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646 · see citing works
Self-citationFairness in Machine Learning: A Survey
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Self-citationSurvey on Machine Learning Biases and Mitigation Techniques
Siddique, S., Haque, M. A., George, R., Gupta, K. D., Gupta, D., & Faruk, M. J. H. (2023) · Digital · doi:10.3390/digital4010001 · Cited by 78 · see citing works
- Authors
- Ashokan, A., & Haas, C.
- Affiliations
- University of Nebraska at Omaha (US)
- Vienna University of Economics and Business (AT)
- Corresponding author
- Ashokan, Ashwathy
- ORCID
- Haas, Christian
- Venue
- Information Processing & Management
- Publisher
- Elsevier BV
- Published
- 2021-09
- Volume
- 58
- Issue
- 5
- Pages
- 102646
- ISSN
- 0020-0271 (ISSN-L)
- 0306-4573
- OpenAlex
- W3166964094
- Cited by
- 47
- References
- 104
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 12 · 2023: 14 · 2022: 7 · 2021: 1
- FWCI
- 10.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Information Processing & Management (publishedVersion)
- WU Research (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
- Keywords
- algorithmic fairness, rating prediction, recommender systems, fairness metrics, bias mitigation, fairness definitions, group fairness
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