# kaal:claim:4855607-031

**Claim.** A precedent and citation WDAG accounting system documents and traces every adjustment to a federated learning model, and that full accounting is what enables dynamic feedback effects and the rapid integration of new techniques.

**Type.** mechanism  **Support.** argued

**Holds when.**

- governance of iteratively updated federated learning models

**Source quote.**

> This setup enables fully accounted dynamic feedback effects for rapid integration of new techniques and approaches to FL. This, in turn, ensures that the models remain cutting-edge and are quickly adaptable to new challenges and opportunities in AI development.

**From.** Wulf A. Kaal, *How AI Models are Optimized Through Web3 Governance* (2024), FL Optimization, page 48

**Cite as.** Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

**Verify.** sha256 of source PDF `eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20How%20AI%20Models%20are%20Optimized%20Through%20Web3%20Governance.pdf

**Topics.** citation-and-knowledge, institutional-design

**Keywords.** wdag, citation-system, feedback-effects, federated-learning, traceability

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