  The success of deep neural nets heavily relies on their ability to encode
complex relations between their input and their output. While this property
serves to fit the training data well, it also obscures the mechanism that
drives prediction. This study aims to reveal hidden concepts by employing an
intervention mechanism that shifts the predicted class based on discrete
variational autoencoders. An explanatory model then visualizes the encoded
information from any hidden layer and its corresponding intervened
representation. By the assessment of differences between the original
representation and the intervened representation, one can determine the
concepts that can alter the class, hence providing interpretability. We
demonstrate the effectiveness of our approach on CelebA, where we show various
visualizations for bias in the data and suggest different interventions to
reveal and change bias.
