Bias Evaluation
Bias Evaluation is a systematic assessment framework within the domain of recommender systems research that identifies, quantifies, and analyzes distortions or systematic deviations in recommendation outcomes arising from algorithmic, data, or user-related factors, thereby distinguishing it from general performance evaluation by focusing specifically on inequities and skewed representations.
234
Publications
20.6K
Citations
759
Authors
342
Institutions
Publications per year
2017–2026
121
234
Batya Friedman, Helen Nissenbaum · ACM Transactions on Information Systems · 1996 · 1.1K citations · Full text
Bias and Fairness in Large Language Models: A Survey
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow et al. · Computational Linguistics · 2024 · 510 citations · Full text
1–5 of 234
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342
University of Science and Technology of China
Hefei, China
6 h-index
20 publications
4.3K citations

Cornell University
Ithaca, United States
5 h-index
16 publications
2.1K citations

Universidad Autónoma de Madrid
Madrid, Spain
4 h-index
9 publications
906 citations
Yale University
New Haven, United States
4 h-index
6 publications
327 citations
University of California, Berkeley
Berkeley, United States
4 h-index
7 publications
404 citations
University of Florida
Gainesville, United States
4 h-index
7 publications
252 citations
Google (United States)
Mountain View, United States
4 h-index
11 publications
753 citations
National University of Singapore
Singapore, Singapore
4 h-index
8 publications
784 citations
1–8 of 342
Repository · 11 publications · 535 citations
11
h-index
5 publications · 365 citations
5
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4 publications · 97 citations
4
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AMR · 4 publications · 609 citations
4
h-index
4 publications · 139 citations
4
h-index
1–5