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The Who in XAI: How AI Background Shapes Perceptions of AI Explanations
Explainability of AI systems is critical for users to take informed actions.
Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions
The second study further explores user preferences between the generated rationales with regard to confidence in the autonomous agent, communicating failure and unexpected behavior.
Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations
Results of these evaluations show that neural machine translation is able to accurately generate rationalizations that describe agent behavior, and that rationalizations are more satisfying to humans than other alternative methods of explanation.
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