Design-Based Supervised Learning with Noisy Human Labels

2026-07-20 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Mathematics & Computational Sciences · Depth: Expert, extended

Summary

Partially Adjudicated Design-Based Supervised Learning (PA-DSL) is a novel method addressing noisy human labels in automated classifier-based statistical analysis. It extends existing rectification techniques by accounting for situations where human audit labels are themselves imperfect and only a subset undergoes expert adjudication. PA-DSL uses adjudicated cases to correct these noisy human labels, then applies this corrected audit information to debias analyses derived from the full set of automated labels. Experiments on synthetic data and the Wikipedia Detox semi-synthetic corpus demonstrate PA-DSL's effectiveness, maintaining nominal coverage and reducing RMSE by 10–17% compared to using only adjudicated labels, especially when noisy human labels contain recoverable signal. The method formalizes a three-tier nested measurement design and proves its design-validity for downstream estimating equations.

Key takeaway

For Research Scientists or Data Scientists building annotation pipelines, PA-DSL offers a robust solution for debiasing analyses when human audit labels are noisy and partially adjudicated. You should consider implementing PA-DSL to improve the efficiency of your downstream estimates, especially if your audit-tier features provide significant signal beyond basic covariates. This approach ensures design validity while leveraging unadjudicated human labels, preventing the severe undercoverage seen with naive plug-in methods or regression-only corrections.

Key insights

PA-DSL debiases analyses from noisy automated labels by nesting corrections for imperfect human audits.

Principles

• Design validity requires known audit and adjudication probabilities.
• Efficiency gains occur when audit-tier features predict adjudicated labels.
• Nuisance functions affect efficiency, not design validity.

Method

PA-DSL uses an inner correction to create audit-level pseudo-labels from adjudicated data, then an outer correction applies these to debias full-population surrogate labels for downstream inference.

In practice

• Target audit rates of π∈[0.10,0.20].
• Use disagreement-driven adjudication at ρ∈[0.10,0.25].
• Record realized audit and adjudication probabilities.

Topics

• Design-Based Supervised Learning
• Noisy Labels
• Human Annotation
• Label Adjudication
• Statistical Debiasing
• RMSE Reduction

Best for: AI Scientist, Data Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.