Simple and near-optimal algorithms for hidden stratification and multi-group learning

Much of the success of modern machine learning has been measured by improvements in accuracy for various classification tasks. Across domains as diverse as image classification and text translation, machine learning models are achieving incredible levels of accuracy; in some cases, they have outperformed humans in visual recognition tasks (Ewerth et al., 2017). However, accuracy is an aggregate statistic that often obscures the underlying structure of mistaken predictions. Oakden-Rayner et al. (2020) recently raised this concern in the context of medical image analysis. Consider the problem of diagnosing a image as being indicative of lung cancer or not.