kaal:claim:4941807-001

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.

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.

Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024), INTRODUCTION, p. 2
https://ssrn.com/abstract=4941807 · source PDF

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

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

failuresupport: arguedfailure: ex-post oversight gapfamily: regulatory-laggovernance-designai-and-agentsrisk-and-incentives

• restates kaal:claim:4796714-004 Ex-post governance, which applies regulation only after AI systems are developed and deployed or after large l...
• restates kaal:claim:4796714-027 Post-deployment monitoring, the standard fallback when ex-ante rules prove inadequate, is typically woefully o...

The quote above is an exact substring of the source PDF, whose sha256 is ab66c1e99a88da1fa36b0c6b536df5184231fe6aa427f3dd53287a4e0ac79853. Extraction method: pdf-text-layer.
Attestation record: colloquium/attestations/006a2cd14ec2c1c0...json
Verify the binding yourself: curl -s https://wulfkaal.github.io/claims/4941807-001.md | sha256sum