  Explainability and comprehensibility of AI are important requirements for
intelligent systems deployed in real-world domains. Users want and frequently
need to understand how decisions impacting them are made. Similarly it is
important to understand how an intelligent system functions for safety and
security reasons. In this paper, we describe two complementary impossibility
results (Unexplainability and Incomprehensibility), essentially showing that
advanced AIs would not be able to accurately explain some of their decisions
and for the decisions they could explain people would not understand some of
those explanations.
