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Prevention is better than cure. This old truth applies not only to theprevention of diseases but also to the prevention of issues with AI models usedin medicine. The source of malfunctioning of predictive models often lies notin the training process but reaches the data acquisition phase or design of theexperiment phase.  In this paper, we analyze in detail a single use case - a Kaggle competitionrelated to the detection of abnormalities in X-ray lung images. We demonstratehow a series of simple tests for data imbalance exposes faults in the dataacquisition and annotation process. Complex models are able to learn suchartifacts and it is difficult to remove this bias during or after the training.Errors made at the data collection stage make it difficult to validate themodel correctly.  Based on this use case, we show how to monitor data and model balance(fairness) throughout the life cycle of a predictive model, from dataacquisition to parity analysis of model scores.

Prevention is better than cure: a case study of the abnormalities detection in the chest