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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)

Paper ID
6445
Venue
SIGMOD
Year
2022
Pagerank
5.6772818e-05
Overall Rank
7,192 | 50.66%
DOI
10.1145/3514221.3520170

Incoming Non-self Citations Over Time

Authors

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}
}

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