Interpretable Data-Based Explanations for Fairness Debugging
Summary: Gopher yields compact data explanations for model bias by pinpointing training-subset root-causes. It defines causal responsibility and uses pruned-pattern mining with ML-based approximation to enable interventions for fairness debugging. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Romila Pradhan (Purdue University)
- 2. Jiongli Zhu (University of California San Diego)
- 3. Boris Glavic (Illinois Institute of Technology)
- 4. Babak Salimi (University of California San Diego)
BibTeX Citation
@inproceedings{pradhan_sigmod22,
title = {{Interpretable Data-Based Explanations for Fairness Debugging}},
author = {Pradhan, Romila and Zhu, Jiongli and Glavic, Boris and Salimi, Babak},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3517886},
url = {https://dl.acm.org/doi/10.1145/3514221.3517886},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 17 of 17 citing papers.
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 7,838 | Consistent Range Approximation for Fair Predictive Modeling | 2023 | VLDB |
| 2 | 6,576 | Toward Interpretable and Actionable Data Analysis with Explanations and Causality | 2022 | VLDB |
| 3 | 9,773 | Fair and Actionable Causal Prescription Ruleset | 2025 | SIGMOD |
| 4 | 11,353 | Reconstructing and Querying ML Pipeline Intermediates | 2023 | CIDR |
| 5 | 3,000 | Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals | 2021 | SIGMOD |
| 6 | 10,709 | CausalExplain: Causal Explanations of Black-box Models with Training Data Subsets | 2025 | SIGMOD |
| 7 | 3,905 | Automated Feature Engineering for Algorithmic Fairness | 2021 | VLDB |
| 8 | 6,637 | Causal Feature Selection for Algorithmic Fairness | 2022 | SIGMOD |
| 9 | 863 | Interventional Fairness : Causal Database Repair for Algorithmic Fairness | 2019 | SIGMOD |
| 10 | 7,192 | Generating Interpretable Data-Based Explanations for Fairness Debugging using Gopher | 2022 | SIGMOD |