Deep learning models have shown great potential for image-based diagnosis assisting clinical decision making. At the same time, an increasing number of reports raise concerns about the potential risk that machine learning could amplify existing health disparities due to human biases that are embedded in the training data. It is of great importance to carefully investigate the extent to which biases may be reproduced or even amplified if we wish to build fair artificial intelligence systems. Seyyed-Kalantari et al. advance this conversation by analysing the performance of a disease classifier across population subgroups. They raise performance disparities related to underdiagnosis as a point of concern; we identify areas from this analysis which we believe deserve additional attention. Specifically, we wish to highlight some theoretical and practical difficulties associated with assessing model fairness through testing on data drawn from the same biased distribution as the training data, especially when the sources and amount of biases are unknown.

该研究通过对人群亚组之间的疾病分类器性能分析，发现低诊断率造成的表现差异引起了关注，进一步强调了在训练数据中嵌入的人类偏见可能会放大现有的健康差异，并探讨了评估模型公正性的一些困难。

在多个数据集偏差源存在时，调查AI算法的低诊断率