# kaal:claim:4796714-029

**Claim.** A unified governance framework is hard to establish in the federated model because each participating entity maintains its own AI systems and datasets, producing variation in standards, protocols, and formats.

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

**Holds when.**

- applies to federated AI architectures with independent participants
- standardization is presupposed by interoperability and ethical practice

**Source quote.**

> In a federated AI model, different entities or organizations maintain their own AI systems and datasets. This can lead to variations in standards, protocols, and formats used, making it difficult to establish a unified governance framework.

**From.** Wulf A. Kaal, *AI Governance* (2024), Adapting Decentralization of AI Governance to AI Models, page 33

**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.** federated standards divergence  (family: harmonization-and-standardization-failure)

**Topics.** governance-design

**Keywords.** federated-learning, standardization, interoperability, governance-fragmentation

**Related claims.**

- restated_by: https://wulfkaal.github.io/claims/4941807-021

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