Generating Interpretable Data-Based Explanations for Fairness Debugging using Gopher
Summary: Gopher generates compact, interpretable, causal explanations for ML fairness by identifying the top-k coherent training-data subsets that are root causes. It quantifies removal/updating effects on bias, outlines an end-to-end architecture, and provides open-source code and a demo. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiongli Zhu (University of California San Diego)
- 2. Romila Pradhan (Purdue University)
- 3. Boris Glavic (Illinois Institute of Technology)
- 4. Babak Salimi (University of California San Diego)
BibTeX Citation
@inproceedings{zhu_sigmod22,
title = {{Generating Interpretable Data-Based Explanations for Fairness Debugging using Gopher}},
author = {Zhu, Jiongli and Pradhan, Romila 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 = {10.1145/3514221.3520170},
url = {https://dl.acm.org/doi/10.1145/3514221.3520170},
year = {2022}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,709 | CausalExplain: Causal Explanations of Black-box Models with Training Data Subsets | 2025 | SIGMOD | 5.093636e-05 |
| 10,796 | Understanding the Black Box: A Deep Empirical Dive into Shapley Value Approximations for Tabular Data | 2025 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,951 | Interpretable Data-Based Explanations for Fairness Debugging | 2022 | SIGMOD | 9.4252389e-05 |
| 2,273 | SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging | 2021 | SIGMOD | 8.8230899e-05 |
| 3,000 | Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals | 2021 | SIGMOD | 7.8677069e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 6,709 | Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification | 2022 | SIGMOD |
| 2 | 13,458 | DENOUNCER: Detection of Unfairness in Classifiers | 2021 | VLDB |
| 3 | 10,709 | CausalExplain: Causal Explanations of Black-box Models with Training Data Subsets | 2025 | SIGMOD |
| 4 | 13,306 | Interactive Fairness Auditing: Leveraging AVOIR for Dynamic Evaluation and Mitigation | 2025 | SIGMOD |
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| 9 | 11,353 | Reconstructing and Querying ML Pipeline Intermediates | 2023 | CIDR |
| 10 | 1,951 | Interpretable Data-Based Explanations for Fairness Debugging | 2022 | SIGMOD |