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Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance.

, , , and . Frontiers Comput. Sci., (2023)

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Improving Users' Mental Models of Intelligent Software Tools., and . IEEE Intelligent Systems, 26 (2): 77-83 (2011)The calibration of trust in an automated system: A sensemaking process., , , and . CTS, page 390-395. IEEE, (2010)Cognitive Anthropomorphism of AI: How Humans and Computers Classify Images.. CoRR, (2020)Explaining AI as an Exploratory Process: The Peircean Abduction Model., , and . CoRR, (2020)Development of the PEBL Traveling Salesman Problem Computerized Testbed., , , and . J. Probl. Solving, (2015)Adapting the task-taxon-task methodology to model the impact of chemical protective gear., , , , , and . Comput. Math. Organ. Theory, 17 (3): 251-271 (2011)Principles of Explanation in Human-AI Systems., , , , , , and . CoRR, (2021)Explainable AI: roles and stakeholders, desirements and challenges., , , , and . Frontiers Comput. Sci., (2023)Collaborative Explainable AI: A Non-algorithmic Approach to Generating Explanations of AI., , and . HCI (44), volume 1498 of Communications in Computer and Information Science, page 144-150. Springer, (2021)Testing the effectiveness of crossword games on immediate and delayed memory for scientific vocabulary and concepts., and . CogSci, cognitivesciencesociety.org, (2018)