  For real-world applications of machine learning (ML), it is essential that
models make predictions based on well-generalizing features rather than
spurious correlations in the data. The identification of such spurious
correlations, also known as shortcuts, is a challenging problem and has so far
been scarcely addressed. In this work, we present a novel approach to detect
shortcuts in image and audio datasets by leveraging variational autoencoders
(VAEs). The disentanglement of features in the latent space of VAEs allows us
to discover feature-target correlations in datasets and semi-automatically
evaluate them for ML shortcuts. We demonstrate the applicability of our method
on several real-world datasets and identify shortcuts that have not been
discovered before.
