Post-hoc Counterfactual Generation with Supervised Autoencoder

Nowadays, AI is increasingly being used in many fields to automate decisions that largely affect the daily lives of humans. The inherent complexity of these systems makes them so-called black-box models. Explainable Artificial Intelligence (XAI) aims at solving this issue by providing methods to overcome this lack of transparency. Counterfactual explanation is a common and well-known class of explanations that produces actionable and understandable explanations for end-users. However, generating realistic and useful counterfactuals remains a challenge. In this work, we investigate the problem of generating counterfactuals that are both close to the data distribution, and to the distribution of the target class. Our objective is to obtain counterfactuals with likely values (i.e. realistic). We propose a model agnostic method for generating realistic counterfactuals by using class prototypes. The novelty of this approach is that these class prototypes are obtained using a supervised auto-encoder. Then, we performed an empirical evaluation across several interpretability metrics, that shows competitive results with the state-of-the-art method.