# kaal:claim:4941807-005

**Claim.** Integrating feedback directly into governance processes allows stakeholders to iteratively adjust AI models as new information, operational experience, and changed environments arrive, which mitigates risks and biases more effectively than static ex-post regulatory frameworks.

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

**Holds when.**

- where feedback is embedded in the governance process itself
- where models can still be adjusted after deployment

**Source quote.**

> stakeholders can iteratively improve and adjust AI models in response to new information, operational experiences, and changing environments. This ongoing process helps mitigate risks and biases more effectively than static, ex-post regulatory frameworks.

**From.** Wulf A. Kaal, *AI Governance Via Web3 Reputation System* (2024), INTRODUCTION, page 3

**Cite as.** Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807

**Verify.** sha256 of source PDF `ab66c1e99a88da1fa36b0c6b536df5184231fe6aa427f3dd53287a4e0ac79853` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20AI%20Governance%20Via%20Web3%20Reputation%20System.pdf

**Topics.** governance-design, ai-and-agents

**Keywords.** dynamic-feedback, ai-governance, iterative-adjustment, bias-mitigation

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