Algorithmic Data Bias
Algorithmic Data Bias is a systematic error or distortion in data processing and decision-making that arises from the design, implementation, or training data of algorithms, leading to unfair or skewed outcomes in data-driven systems.
154
Publications
13.3K
Citations
656
Authors
333
Institutions
Publications per year
2017–2026
146
Parents
154
Out of One, Many: Using Language Models to Simulate Human Samples
Lisa P. Argyle, Ethan C. Busby, Nancy Fulda et al. · Political Analysis · 2023 · 570 citations · Full text
Ethical Considerations of Using ChatGPT in Health Care
Changyu Wang, Siru Liu, Hao Yang et al. · Journal of Medical Internet Research · 2023 · 508 citations · Full text
Deep Isolation Forest for Anomaly Detection
Hongzuo Xu, Guansong Pang, Yijie Wang et al. · IEEE Transactions on Knowledge and Data Engineering · 2023 · 451 citations
1–5 of 154
656
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3
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3
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3
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Rutgers, The State University of New Jersey, United States
3 publications · 127 citations
3
h-index
1–5 of 656
333
Stanford University
Stanford, United States
5 h-index
6 publications
693 citations

University of Toronto
Toronto, Canada
5 h-index
8 publications
1.7K citations
University of Oxford
Oxford, United Kingdom
5 h-index
8 publications
831 citations
University of Michigan
Ann Arbor, United States
4 h-index
4 publications
404 citations
Massachusetts Institute of Technology
Cambridge, United States
4 h-index
8 publications
1.2K citations

Universitat Pompeu Fabra
Barcelona, Spain
4 h-index
6 publications
317 citations
University of Cambridge
Cambridge, United Kingdom
4 h-index
7 publications
1.3K citations
Columbia University
New York, United States
4 h-index
9 publications
857 citations
1–8 of 333
Repository · 10 publications · 334 citations
10
h-index
Repository · 3 publications · 152 citations
3
h-index
3 publications · 171 citations
3
h-index
2 publications · 904 citations
2
h-index
2 publications · 83 citations
2
h-index
1–5