Safety-Critical Systems

Towards Verified and Targeted Explanations through Formal Methods

This paper introduces ViTaX, a formal XAI framework designed to generate targeted semifactual explanations with mathematical guarantees. It addresses the shortcomings of existing XAI methods in providing trustworthy explanations for deep neural networks in safety-critical domains like autonomous driving and medical diagnosis.