Fault Lines: Benchmarking the Impact of Label Data Quality on ML Robustness and Fairness
Summary: FAULT LINES: a model-agnostic benchmark (15 datasets, systematic diverse label corruptions) plus an evaluation suite to measure robustness and fairness across 22 SOTA classifiers. Key result: many models resist random noise but <10% biased noise causes large accuracy and fairness losses; transformers often handle biased noise better than GBDTs but with higher tuning-dependent variance. (summarized by gpt-5-mini on Mar 13 2026)
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Authors
- 1. David Jackson
- 2. Paul Groth
- 3. Hazar Harmouch
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,406 | Responsible Data Management | 2020 | VLDB | 0.0001216385 |
| 3,397 | Automatic Data Repair: Are We Ready to Deploy? | 2024 | VLDB | 7.1386386e-05 |
| 6,552 | How do Categorical Duplicates Affect ML? A New Benchmark and Empirical Analyses | 2024 | VLDB | 5.0109216e-05 |
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