arXiv:2509.11379v3 Announce Type: replace-cross Abstract: We demonstrate that learning procedures that rely on aggregated labels, e.g., label information distilled from noisy responses, enjoy robustness properties impossible without data cleaning. This robustness appears in several ways. In the context of risk consistency -- when one takes the standard approach in machine learning of minimizing a surrogate (typically convex) loss in place of a desired task loss (such as the zero-one mis-classification error) -- procedures using label aggregation obtain stronger consistency guarantees than thos
The continuous growth in machine learning applications and the increasing availability of large, often noisy datasets necessitate improved methods for data quality and model robustness, making label cleaning research highly relevant.
Sophisticated readers should care as this research promises more robust and reliable AI systems, reducing errors and improving decision-making in critical applications, which directly impacts the trustworthiness and adoption of AI.
Learning procedures will become more resilient to imperfect data, potentially enabling AI to operate effectively with less pristine datasets and reducing the intensive human effort needed for data annotation.
- · AI developers
- · Industries relying on large, noisy datasets (e.g., healthcare, finance)
- · AI ethics and safety researchers
- · Companies offering pure data labeling services without quality enhancement tools
- · AI models highly sensitive to data noise
AI models will achieve higher accuracy and reliability in real-world, complex scenarios due to improved label robustness.
The cost and time associated with preparing high-quality training data for AI projects could decrease significantly.
Broader adoption of AI in sensitive domains where data quality is paramount will accelerate, potentially leading to new applications.
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Read at arXiv cs.LG