# Model performance

`kaal:entity:model-performance`

**Status.** derived

This node is assembled mechanically from the 3 claims that carry the concept tag `model-performance`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself.

## Every claim under this term

3 claims across 3 works, 2024 to 2025.

**2024**

- [4855607-040](https://wulfkaal.github.io/claims/4855607-040) [condition/argued] -- Governance protocols themselves must be continuously evaluated and adapted, because without that ongoing revision AI models will neither maintain optimal performance nor stay in line with changing regulations and societal expectations.
  > Moreover, continuously evaluating and adapting governance protocols is vital to maintain optimal performance of AI models and ensure they stay in line with changing regulations and societal expectations.
  Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607
- [4941807-010](https://wulfkaal.github.io/claims/4941807-010) [failure/argued] *(failure mode)* -- Strict data privacy regulation such as the GDPR imposes stringent conditions on data sharing that limit the amount and variety of data available to AI systems, which can reduce model performance and exacerbate bias because the training dataset is restricted.
  > GDPR imposes stringent conditions on data sharing, which can limit the amount and variety of data AI systems use, potentially reducing their performance and exacerbating biases due to the restricted dataset.
  Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807

**2025**

- [5095633-023](https://wulfkaal.github.io/claims/5095633-023) [failure/argued] *(failure mode)* -- 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). SSRN: https://ssrn.com/abstract=5095633

## Verify

Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust.

    curl -s https://wulfkaal.github.io/entities/model-performance.md | sha256sum

**Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.