kaal:claim:5095633-023

Maintaining consistent annotation quality across many annotators and automated systems is unsolved at scale, and small labeling errors translate into significant degradation of model performance in critical applications.

With the scale at which data annotation occurs, maintaining consistent quality across different annotators or automated systems remains a practical challenge. Small errors in annotation can have significant impacts on model performance

Wulf A. Kaal, Artificial Intelligence The Final Frontier (2025), 4.2. Practical Shortcomings, p. 13
https://ssrn.com/abstract=5095633 · source PDF

Wulf A. Kaal, Artificial Intelligence The Final Frontier (2025). SSRN: https://ssrn.com/abstract=5095633

• large-scale annotation operations
• safety-critical applications such as autonomous driving

failuresupport: arguedfailure: annotation quality driftfamily: data-quality-and-comparabilityai-and-agents

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