# kaal:claim:4796714-038

**Claim.** Broad community governance of AI training identifies and mitigates bias more effectively than data validation alone, because validation focused approaches can overlook systemic biases already embedded in the pretrained model.

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

**Holds when.**

- compares community governance of training against post hoc data validation
- concerns systemic bias baked in during pretraining

**Source quote.**

> Moreover, involving a broad community in the governance of AI training can help identify and mitigate biases more effectively than a focus on data validation alone, which might overlook systemic biases embedded in the pretrained models.

**From.** Wulf A. Kaal, *AI Governance* (2024), Model 3: Ex-Ante Community AI Governance, page 51

**Cite as.** Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714

**Verify.** sha256 of source PDF `59fa63bae179e8f9b6b8efbdf90cee28400276512a1b04f9f579a48641305c93` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20AI%20Governance.pdf

**Failure mode.** systemic bias invisible to validation  (family: ai-model-and-training-failure)

**Topics.** governance-design

**Keywords.** bias-mitigation, community-governance, pretrained-models, systemic-bias, data-validation

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