# kaal:claim:4941807-010

**Claim.** Strict data privacy regulation such as the GDPR imposes stringent conditions on data sharing that limit the amount and variety of data available to AI systems, which can reduce model performance and exacerbate bias because the training dataset is restricted.

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

**Holds when.**

- under stringent data sharing regimes such as the GDPR

**Source quote.**

> GDPR imposes stringent conditions on data sharing, which can limit the amount and variety of data AI systems use, potentially reducing their performance and exacerbating biases due to the restricted dataset.

**From.** Wulf A. Kaal, *AI Governance Via Web3 Reputation System* (2024), ORIGIN OF AI, page 8

**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.** privacy driven data starvation  (family: ai-model-and-training-failure)

**Topics.** consensus-and-security

**Keywords.** gdpr, data-privacy, algorithmic-bias, model-performance, regulatory-tradeoffs

**Related claims.**

- extends: https://wulfkaal.github.io/claims/4796714-008

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