# kaal:claim:4796714-011

**Claim.** Bias in AI systems arises when algorithms incorporate discriminatory practices carried in their training data, and the resulting outputs reveal a profound misalignment between AI operations and societal values, ethics, and norms.

**Type.** mechanism  **Support.** evidenced

**Holds when.**

- arises through the training data pathway
- observable in deployed systems such as chatbots and screening tools

**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* (2024), Shortcomings in Existing AI Governance, page 17

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

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

**Keywords.** bias, training-data, value-alignment, discrimination

**Related claims.**

- restated_by: https://wulfkaal.github.io/claims/4941807-015
- generalizes: https://wulfkaal.github.io/claims/5541658-030

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