# Pretrained models

`kaal:entity:pretrained-models`

**Status.** derived

This node is assembled mechanically from the 3 claims that carry the concept tag `pretrained-models`. 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 1 works, 2024 to 2024.

**2024**

- [4796714-004](https://wulfkaal.github.io/claims/4796714-004) [failure/argued] *(failure mode)* -- Ex-post governance, which applies regulation only after AI systems are developed and deployed or after large language models have been pretrained on existing proprietary datasets, fails to address risks and biases preemptively.
  > The traditional ex-post governance methods, where regulations are applied after AI systems are developed and deployed, or were pretrained LLMs models were trained on existing proprietary datasets, often fall short in preemptively addressing risks and biases.
  Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714
- [4796714-037](https://wulfkaal.github.io/claims/4796714-037) [failure/argued] *(failure mode)* -- A decentralized data validation layer applied to pretrained models is efficient but structurally limited: because it cannot drive significant changes to the model's core design or training approach, it leaves the model more attack prone.
  > The validation layer approach, while efficient for refining pretrained models, may not facilitate significant changes in the model's core design or training approach which could make it more attack prone.
  Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714
- [4796714-038](https://wulfkaal.github.io/claims/4796714-038) [mechanism/argued] *(failure mode)* -- Broad community governance of AI training identifies and mitigates bias more effectively than data validation alone, because validation focused approaches can overlook systemic biases already embedded in the pretrained model.
  > Moreover, involving a broad community in the governance of AI training can help identify and mitigate biases more effectively than a focus on data validation alone, which might overlook systemic biases embedded in the pretrained models.
  Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714

## 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/pretrained-models.md | sha256sum

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