Automated Rationale Generation: Moving Towards Explainable AI

With the advent of AI, it has become imperative for the progression of a parallel field, Explainable AI (XAI), to foster trust and human-interpretability in the workings of these intelligent systems. These explanations help the human collaborator understand the circumstances that led to any unexpected behavior in the system and allows the operator to make an informed decision. This article summarizes the paper that demonstrates an approach to generating natural language real-time rationale from autonomous agents in sequential problems and evaluates their humanlike-ness. Using the context of an agent that plays Frogger, a corpus of explanations is collected and fed to a neural rationale generator to produce rationales. These are then studied to measure user perceptions of confidence, humanlike-ness, etc.