Aug. 11, 2023, 6:43 a.m. | Nina Weng, Siavash Bigdeli, Eike Petersen, Aasa Feragen

cs.LG updates on arXiv.org arxiv.org

While many studies have assessed the fairness of AI algorithms in the medical
field, the causes of differences in prediction performance are often unknown.
This lack of knowledge about the causes of bias hampers the efficacy of bias
mitigation, as evidenced by the fact that simple dataset balancing still often
performs best in reducing performance gaps but is unable to resolve all
performance differences. In this work, we investigate the causes of gender bias
in machine learning-based chest X-ray diagnosis. …

ai algorithms algorithms arxiv bias diagnosis differences fairness gender gender bias knowledge medical performance prediction ray sex studies x-ray

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