Published July 9, 2026
| Version v1.0.0
Software
Open
Fairness Auditing in Tabular Machine Learning: A Multi-Dataset Benchmark of Bias, Mitigation, and Robustness
Authors/Creators
Description
Initial public release of the reproducibility package for the manuscript: "Fairness Auditing in Public Tabular Benchmarks: A Multi-Dataset Study of Bias and Mitigation".
This release includes processed result tables, post-processing code, generated figures, robustness analyses, dataset provenance, fingerprints, and documentation. Raw public benchmark datasets are not redistributed.
Notes
If you use this dataset or code, please cite the associated manuscript.
Files
NullSpace20/fairness-auditing-public-tabular-benchmarks-v1.0.0.zip
Additional details
Related works
- Is supplement to
- Software: https://github.com/NullSpace20/fairness-auditing-public-tabular-benchmarks/tree/v1.0.0 (URL)