MithraCoverage: A System for Investigating Population Bias for Intersectional Fairness
Summary: MithraCoverage identifies intersectional subgroups with inadequate representation by coverage over attributes. A web-based visualization lets data scientists explore datasets and diagnose population bias via underrepresented intersectional groups. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhongjun Jin (University of Michigan)
- 2. Mengjing Xu (University of Michigan)
- 3. Chenkai Sun (University of Michigan)
- 4. Abolfazl Asudeh (University of Illinois Chicago)
- 5. H. V. Jagadish (University of Michigan)
BibTeX Citation
@inproceedings{jin_sigmod20,
title = {{MithraCoverage: A System for Investigating Population Bias for Intersectional Fairness}},
author = {Jin, Zhongjun and Xu, Mengjing and Sun, Chenkai and Asudeh, Abolfazl and Jagadish, H. V.},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3384689},
url = {https://dl.acm.org/doi/10.1145/3318464.3384689},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,273 | SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging | 2021 | SIGMOD | 8.8230899e-05 |
| 3,190 | Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence | 2021 | SIGMOD | 7.6532441e-05 |
| 5,510 | Responsible Data Integration: Next-generation Challenges | 2022 | SIGMOD | 6.1919207e-05 |
| 5,631 | Tailoring Data Source Distributions for Fairness-aware Data Integration | 2021 | VLDB | 6.1427477e-05 |
| 6,824 | Fairly Evaluating and Scoring Items in a Data Set | 2020 | VLDB | 5.7623529e-05 |
| 7,367 | Identifying Insufficient Data Coverage for Ordinal Continuous-Valued Attributes | 2021 | SIGMOD | 5.6319194e-05 |
| 7,838 | Consistent Range Approximation for Fair Predictive Modeling | 2023 | VLDB | 5.5343411e-05 |
| 11,419 | Equitable Top-k Results for Long Tail Data | 2023 | SIGMOD | 5.093636e-05 |
| 11,721 | How Divergent Is Your Data? | 2021 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|
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