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
- 2. Mengjing Xu
- 3. Chenkai Sun
- 4. Abolfazl Asudeh
- 5. H. V. Jagadish
Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,942 | SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging | 2021 | SIGMOD | 0.00010010569 |
| 3,170 | Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence | 2021 | SIGMOD | 7.4517805e-05 |
| 5,982 | Responsible Data Integration: Next-generation Challenges | 2022 | SIGMOD | 5.2409386e-05 |
| 6,462 | Tailoring Data Source Distributions for Fairness-aware Data Integration | 2021 | VLDB | 5.0479645e-05 |
| 6,895 | Identifying Insufficient Data Coverage for Ordinal Continuous-Valued Attributes | 2021 | SIGMOD | 4.8879337e-05 |
| 7,665 | Fairly Evaluating and Scoring Items in a Data Set | 2020 | VLDB | 4.6799478e-05 |
| 7,853 | Consistent Range Approximation for Fair Predictive Modeling | 2023 | VLDB | 4.6308623e-05 |
| 11,220 | Equitable Top-k Results for Long Tail Data | 2023 | SIGMOD | 4.1905499e-05 |
| 11,527 | How Divergent Is Your Data? | 2021 | VLDB | 4.1905499e-05 |
Previous
Page 1 / 1
Next
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 |
|---|
Previous
Page 1 / 1
Next