# kaal:claim:4941807-001

**Claim.** Ex-post AI governance, in which regulation is applied only after AI systems have been developed and deployed or after large language models have already been pretrained on existing proprietary datasets, falls short of preemptively addressing the risks and biases those systems carry.

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

**Holds when.**

- when regulatory intervention occurs after development and deployment
- when pretrained models were trained on existing proprietary datasets

**Source quote.**

> The traditional ex-post governance methods, where regulations are applied after AI systems are developed and deployed, or were pretrained LLMs models were trained on existing proprietary datasets, often fall short in preemptively addressing risks and biases.

**From.** Wulf A. Kaal, *AI Governance Via Web3 Reputation System* (2024), INTRODUCTION, page 2

**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

**Failure mode.** ex-post oversight gap  (family: regulatory-lag)

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

**Keywords.** ai-governance, ex-post-regulation, regulatory-timing, algorithmic-bias, risk-mitigation

**Related claims.**

- restates: https://wulfkaal.github.io/claims/4796714-004
- restates: https://wulfkaal.github.io/claims/4796714-027

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