Towards Robust Contrastive Explanations for Human-Neural Multi-agent Systems

Generating explanations of high quality is fundamental to the development of trustworthy human-AI interactions. We here study the problem of generating contrastive explanations with formal robustness guarantees. We formalise a new notion of robustness and introduce two novel verification-based algorithms to (i) identify non-robust explanations generated by other methods and (ii) generate contrastive explanations augmented with provable robustness certificates. We present an implementation and evaluate the utility of the approach on two case studies concerning neural agents trained on credit scoring and image classification tasks.