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In this study, we take a departure and explore an explainability-drivenstrategy to data auditing, where actionable insights into the data at hand arediscovered through the eyes of quantitative explainability on the behaviour ofa dummy model prototype when exposed to data. We demonstrate this strategy byauditing two popular medical benchmark datasets, and discover hidden dataquality issues that lead deep learning models to make predictions for the wrongreasons. The actionable insights gained from this explainability driven dataauditing strategy is then leveraged to address the discovered issues to enablethe creation of high-performing deep learning models with appropriateprediction behaviour. The hope is that such an explainability-driven strategycan be complimentary to data-driven strategies to facilitate for moreresponsible development of machine learning algorithms for computer visionapplications.

Insights into Data through Model Behaviour: An Explainability-driven Strategy for Data Auditing for Responsible Computer Vision Applications