  Counterfactual explanations for machine learning models are used to find
minimal interventions to the feature values such that the model changes the
prediction to a different output or a target output. A valid counterfactual
explanation should have likely feature values. Here, we address the challenge
of generating counterfactual explanations that lie in the same data
distribution as that of the training data and more importantly, they belong to
the target class distribution. This requirement has been addressed through the
incorporation of auto-encoder reconstruction loss in the counterfactual search
process. Connecting the output behavior of the classifier to the latent space
of the auto-encoder has further improved the speed of the counterfactual search
process and the interpretability of the resulting counterfactual explanations.
Continuing this line of research, we show further improvement in the
interpretability of counterfactual explanations when the auto-encoder is
trained in a semi-supervised fashion with class tagged input data. We
empirically evaluate our approach on several datasets and show considerable
improvement in-terms of several metrics.
