SIGMOD 2020 Tutorial on Fairness and Bias in Peer Review and other Sociotechnical Intelligent Systems
Summary: SIGMOD 2020 tutorial on fairness and bias at the data-management–sociotechnical interface, defining fairness via protected characteristics and surveying auditing methods. It highlights bias in peer review and distributed evaluations and examines how data collection shapes outcomes. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Nihar B. Shah (Carnegie Mellon University)
- 2. Zachary Lipton (Carnegie Mellon University)
BibTeX Citation
@inproceedings{shah_sigmod20,
title = {{SIGMOD 2020 Tutorial on Fairness and Bias in Peer Review and other Sociotechnical Intelligent Systems}},
author = {Shah, Nihar B. and Lipton, Zachary},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3383129},
url = {https://dl.acm.org/doi/10.1145/3318464.3383129},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,489 | Identifying Insufficient Data Coverage for Ordinal Continuous-Valued Attributes | 2021 | SIGMOD | 5.5113546e-05 |
| 10,032 | Maximizing Fair Content Spread via Edge Suggestion in Social Networks | 2022 | VLDB | 5.0925155e-05 |
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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