Abstract: The goal of this workshop is to facilitate discussion regarding how formal methods can be used to increase predictability, explainability, and accountability of ML-enabled autonomous systems. The workshop welcomes results ranging from concept formulation (by connecting these concepts with existing research topics in verification, logic and game theory), through algorithms, methods and tools for analyzing ML-enabled systems, to concrete case studies and examples.