Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms
This is an incremental critique highlighting methodological flaws in bias analysis for healthcare algorithms, relevant to researchers and practitioners in fair ML.
The paper critiques a study on machine learning bias in chest X-ray diagnosis, arguing that the experimental setup is insufficient to investigate algorithmic underdiagnosis due to dataset bias and random data splitting.
An increasing number of reports raise concerns about the risk that machine learning algorithms could amplify health disparities due to biases embedded in the training data. Seyyed-Kalantari et al. find that models trained on three chest X-ray datasets yield disparities in false-positive rates (FPR) across subgroups on the 'no-finding' label (indicating the absence of disease). The models consistently yield higher FPR on subgroups known to be historically underserved, and the study concludes that the models exhibit and potentially even amplify systematic underdiagnosis. We argue that the experimental setup in the study is insufficient to study algorithmic underdiagnosis. In the absence of specific knowledge (or assumptions) about the extent and nature of the dataset bias, it is difficult to investigate model bias. Importantly, their use of test data exhibiting the same bias as the training data (due to random splitting) severely complicates the interpretation of the reported disparities.