Design-Based Supervised Learning with Noisy Human Labels
Researchers increasingly use automated classifiers to label unstructured data for statistical analysis. Existing rectification methods can correct errors in these automated labels using a probability-sampled audit set, but they usually treat the audit labels as correct. In practice, human audit labels are often noisy, and only some audited items are reviewed by an expert or adjudicator. We propose Partially Adjudicated Design-Based Supervised Learning (PA-DSL), a method for this setting. It uses adjudicated cases to correct noisy human labels and then uses the corrected audit information to debias analyses based on the full set of automated labels. The estimator is valid for a broad class...
| Authors | Robert Chew, Matthew R. Williams |
|---|---|
| Primary category | stat.ML |
| Published | 16 July 2026 |
| Last updated at source | 16 July 2026 |
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