Fault Lines: Benchmarking the Impact of Label Data Quality on ML Robustness and Fairness
Summary: Fault Lines benchmarks 22 classifiers across 15 datasets with diverse label-noise corruptions, jointly measuring predictive robustness and fairness. Biased noise—even below 10%—is far more damaging than random noise, with architecture-dependent resilience and fairness failures. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. David Jackson (University of Amsterdam)
- 2. Paul Groth (University of Amsterdam)
- 3. Hazar Harmouch (University of Amsterdam)
BibTeX Citation
@article{jackson_vldb26,
title = {{Fault Lines: Benchmarking the Impact of Label Data Quality on ML Robustness and Fairness}},
author = {Jackson, David and Groth, Paul and Harmouch, Hazar},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {4},
pages = {670--683},
doi = {10.14778/3785297.3785308},
url = {https://doi.org/10.14778/3785297.3785308},
year = {2026}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,340 | Responsible Data Management | 2020 | VLDB | 0.00011111667 |
| 3,580 | Automatic Data Repair: Are We Ready to Deploy? | 2024 | VLDB | 7.2888516e-05 |
| 6,928 | How do Categorical Duplicates Affect ML? A New Benchmark and Empirical Analyses | 2024 | VLDB | 5.7370426e-05 |
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