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Automated rationale generation is an approach for real-time explanationgeneration whereby a computational model learns to translate an autonomousagent's internal state and action data representations into natural language.Training on human explanation data can enable agents to learn to generatehuman-like explanations for their behavior. In this paper, using the context ofan agent that plays Frogger, we describe (a) how to collect a corpus ofexplanations, (b) how to train a neural rationale generator to producedifferent styles of rationales, and (c) how people perceive these rationales.We conducted two user studies. The first study establishes the plausibility ofeach type of generated rationale and situates their user perceptions along thedimensions of confidence, humanlike-ness, adequate justification, andunderstandability. The second study further explores user preferences betweenthe generated rationales with regard to confidence in the autonomous agent,communicating failure and unexpected behavior. Overall, we find alignmentbetween the intended differences in features of the generated rationales andthe perceived differences by users. Moreover, context permitting, participantspreferred detailed rationales to form a stable mental model of the agent'sbehavior.

Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions