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The rapid development of Artificial Intelligence (AI) requires developers anddesigners of AI systems to focus on the collaboration between humans andmachines. AI explanations of system behavior and reasoning are vital foreffective collaboration by fostering appropriate trust, ensuring understanding,and addressing issues of fairness and bias. However, various contextual andsubjective factors can influence an AI system explanation's effectiveness. Thiswork draws inspiration from findings in cognitive psychology to understand howeffective explanations can be designed. We identify four components to whichexplanation designers can pay special attention: perception, semantics, intent,and user & context. We illustrate the use of these four explanation componentswith an example of estimating food calories by combining text with visuals,probabilities with exemplars, and intent communication with both user andcontext in mind. We propose that the significant challenge for effective AIexplanations is an additional step between explanation generation usingalgorithms not producing interpretable explanations and explanationcommunication. We believe this extra step will benefit from carefullyconsidering the four explanation components outlined in our work, which canpositively affect the explanation's effectiveness.

Exploring Effectiveness of Explanations for Appropriate Trust: Lessons from Cognitive Psychology