# kaal:claim:4796714-037

**Claim.** 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.

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

**Holds when.**

- applies to Model 2, the decentralized data validation layer approach
- concerns pretrained models whose parameters are already fixed

**Source quote.**

> 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.

**From.** Wulf A. Kaal, *AI Governance* (2024), Model 3: Ex-Ante Community AI Governance, page 51

**Cite as.** Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714

**Verify.** sha256 of source PDF `59fa63bae179e8f9b6b8efbdf90cee28400276512a1b04f9f579a48641305c93` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20AI%20Governance.pdf

**Failure mode.** validation layer scope limit  (family: ai-oversight-and-alignment-gap)

**Topics.** consensus-and-security, governance-design

**Keywords.** data-validation, pretrained-models, attack-surface, governance-scope

**Canonical form.** This markdown file is the canonical hashed representation of the claim. Its sha256 is the content hash used for attestation.