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.

Source quote, verbatim

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.

From

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

Cite as

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

Holds when

clinical deployment on populations underrepresented in training data

Classification

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

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