kaal:claim:5095633-015

In healthcare, biased or stale training data produces algorithms that misdiagnose underrepresented populations and thereby reinforce existing health disparities instead of reducing them.

In healthcare, for instance, biased or stale training data could lead to algorithms that misdiagnose underrepresented populations, reinforcing existing health disparities rather than alleviating them.

Wulf A. Kaal, Artificial Intelligence The Final Frontier (2025), 2.2. Implications for Society and the Future of Humanity, p. 5
https://ssrn.com/abstract=5095633 · source PDF

Wulf A. Kaal, Artificial Intelligence The Final Frontier (2025). SSRN: https://ssrn.com/abstract=5095633

• clinical deployment on populations underrepresented in training data

failuresupport: arguedfailure: clinical bias amplificationfamily: ai-model-and-training-failureai-and-agentsempirical-evidence

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