kaal:claim:4796714-011

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.

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.

Wulf A. Kaal, AI Governance (2024), Shortcomings in Existing AI Governance, p. 17
https://ssrn.com/abstract=4796714 · source PDF

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

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

mechanismsupport: evidencedfailure: training data bias transmissionfamily: ai-model-and-training-failureai-and-agentseducation-and-practice

• restated_by kaal:claim:4941807-015 A critical unsolved challenge for AI governance is bias mitigation, because biases enter inadvertently when al...
• generalizes kaal:claim:5541658-030 Because AI systems are predominantly developed in the West and trained mostly on Western data, their outputs a...

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