  Machine learning models have achieved high overall accuracy in medical image
analysis. However, performance disparities on specific patient groups pose
challenges to their clinical utility, safety, and fairness. This can affect
known patient groups - such as those based on sex, age, or disease subtype - as
well as previously unknown and unlabeled groups. Furthermore, the root cause of
such observed performance disparities is often challenging to uncover,
hindering mitigation efforts. In this paper, to address these issues, we
leverage Slice Discovery Methods (SDMs) to identify interpretable
underperforming subsets of data and formulate hypotheses regarding the cause of
observed performance disparities. We introduce a novel SDM and apply it in a
case study on the classification of pneumothorax and atelectasis from chest
x-rays. Our study demonstrates the effectiveness of SDMs in hypothesis
formulation and yields an explanation of previously observed but unexplained
performance disparities between male and female patients in widely used chest
X-ray datasets and models. Our findings indicate shortcut learning in both
classification tasks, through the presence of chest drains and ECG wires,
respectively. Sex-based differences in the prevalence of these shortcut
features appear to cause the observed classification performance gap,
representing a previously underappreciated interaction between shortcut
learning and model fairness analyses.
