# kaal:claim:4855607-034

**Claim.** Distributing governance across all participants prevents any single entity from dominating decision making, and because model or training changes then require consensus, the resulting decisions reflect collective rather than individual interest.

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

**Holds when.**

- governance systems where change requires consensus among distributed participants

**Source quote.**

> By distributing governance across all participants, the proposed web3 community governance system ensures that no single entity can dominate the decision-making process. This structure promotes the alignment of incentives since changes to the model or the training process require consensus

**From.** Wulf A. Kaal, *How AI Models are Optimized Through Web3 Governance* (2024), RLHF Optimization, page 51

**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.** consensus-and-security, regulatory-failure, ai-and-agents, risk-and-incentives, decentralization, governance-design

**Keywords.** rlhf, consensus, capture-resistance, incentive-alignment, decentralized-governance

**Related claims.**

- restated_by: https://wulfkaal.github.io/claims/4941807-025

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