  This is the Proceedings of ICML 2021 Workshop on Theoretic Foundation,
Criticism, and Application Trend of Explainable AI. Deep neural networks (DNNs)
have undoubtedly brought great success to a wide range of applications in
computer vision, computational linguistics, and AI. However, foundational
principles underlying the DNNs' success and their resilience to adversarial
attacks are still largely missing. Interpreting and theorizing the internal
mechanisms of DNNs becomes a compelling yet controversial topic. This workshop
pays a special interest in theoretic foundations, limitations, and new
application trends in the scope of XAI. These issues reflect new bottlenecks in
the future development of XAI.
