Cited by 47Ethics and Social Impacts of AIRecommender Systems and TechniquesGame Theory and Voting Systems

Fairness metrics and bias mitigation strategies for rating predictions

Ashokan, Ashwathy, Haas, Christian·2021·Information Processing & Management

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Crossref · 10.1016/j.ipm.2021.102646

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104 of 104 references verified0 citation errors · 0 not checkable

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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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Authors
Ashokan, A., & Haas, C.
Affiliations
  • University of Nebraska at Omaha (US)
  • Vienna University of Economics and Business (AT)
Corresponding author
Ashokan, Ashwathy
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
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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