# kaal:claim:5095633-023

**Claim.** 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.

**Type.** failure  **Support.** argued

**Holds when.**

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

**Source quote.**

> 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

**From.** Wulf A. Kaal, *Artificial Intelligence The Final Frontier* (2025), 4.2. Practical Shortcomings, page 13

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

**Verify.** sha256 of source PDF `cbb484711f89bcefc9fc6a5730a1ed0a3f764d7999ad9b6f7d8ea05634c26c63` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202025%20-%20Artificial%20Intelligence%20The%20Final%20Frontier.pdf

**Failure mode.** annotation quality drift  (family: data-quality-and-comparability)

**Topics.** ai-and-agents

**Keywords.** quality-control, annotation-consistency, model-performance, autonomous-driving

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