# kaal:claim:4855607-024

**Claim.** WDAGs allow new regulatory and ethical standards to be integrated into existing AI systems without overhauling the entire model architecture, which is what makes rapid legal adaptation feasible in sectors such as public safety and healthcare.

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

**Holds when.**

- sectors where AI applications must rapidly adapt to new laws and ethical considerations

**Source quote.**

> In the case of federal AI learning models, for instance, WDAGs facilitate the integration of new regulatory and ethical standards into existing AI systems without the need to overhaul the entire model architecture.

**From.** Wulf A. Kaal, *How AI Models are Optimized Through Web3 Governance* (2024), Web3 Governance for AI Model Optimization, page 37

**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.** compliance, ai-and-agents

**Keywords.** wdag, regulatory-adaptation, compliance, model-architecture, healthcare-ai

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