  Explainability for machine learning models has gained considerable attention
within our research community given the importance of deploying more reliable
machine-learning systems. In computer vision applications, generative
counterfactual methods indicate how to perturb a model's input to change its
prediction, providing details about the model's decision-making. Current
counterfactual methods make ambiguous interpretations as they combine multiple
biases of the model and the data in a single counterfactual interpretation of
the model's decision. Moreover, these methods tend to generate trivial
counterfactuals about the model's decision, as they often suggest to exaggerate
or remove the presence of the attribute being classified. For the machine
learning practitioner, these types of counterfactuals offer little value, since
they provide no new information about undesired model or data biases. In this
work, we propose a counterfactual method that learns a perturbation in a
disentangled latent space that is constrained using a diversity-enforcing loss
to uncover multiple valuable explanations about the model's prediction.
Further, we introduce a mechanism to prevent the model from producing trivial
explanations. Experiments on CelebA and Synbols demonstrate that our model
improves the success rate of producing high-quality valuable explanations when
compared to previous state-of-the-art methods. We will publish the code.
