# kaal:claim:4941807-015

**Claim.** A critical unsolved challenge for AI governance is bias mitigation, because biases enter inadvertently when algorithms incorporate discriminatory practices carried in the data used for training.

**Type.** failure  **Support.** evidenced

**Holds when.**

- where training data embeds discriminatory practices

**Source quote.**

> AI governance does encounter a critical challenge in mitigating biases within AI systems, where biases can inadvertently arise through algorithms incorporating discriminatory practices due to data used in training.

**From.** Wulf A. Kaal, *AI Governance Via Web3 Reputation System* (2024), Shortcomings in Existing AI Governance, page 17

**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.** training data bias propagation  (family: ai-model-and-training-failure)

**Topics.** ai-and-agents, education-and-practice, governance-design

**Keywords.** algorithmic-bias, training-data, ai-governance, discrimination, societal-values

**Related claims.**

- restates: https://wulfkaal.github.io/claims/4796714-011
- restates: https://wulfkaal.github.io/claims/4855607-003

**Canonical form.** This markdown file is the canonical hashed representation of the claim. Its sha256 is the content hash used for attestation.