# kaal:claim:4855607-030

**Claim.** The feedback effects that make community governance of federated learning work will not materialize unless expert community members are selected coherently, making coherent expert selection a precondition of the mechanism rather than an optional refinement.

**Type.** condition  **Support.** asserted

**Holds when.**

- community driven governance of federated learning models

**Source quote.**

> The key is to select the expert community members coherently to allow for the feedback effects for FL to materialize.

**From.** Wulf A. Kaal, *How AI Models are Optimized Through Web3 Governance* (2024), FL Optimization, page 47

**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.** institutional-design, governance-design

**Keywords.** expert-selection, feedback-effects, federated-learning, necessary-condition, community-governance

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