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}
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