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Explaining Classifications to Non Experts: An XAI User Study of Post Hoc Explanations for a Classifier When People Lack Expertise
Very few eXplainable AI (XAI) studies consider how users understanding of explanations might change depending on whether they know more or less about the to be explained domain (i. e., whether they differ in their expertise).
Play MNIST For Me! User Studies on the Effects of Post-Hoc, Example-Based Explanations & Error Rates on Debugging a Deep Learning, Black-Box Classifier
This paper reports two experiments (N=349) on the impact of post hoc explanations by example and error rates on peoples perceptions of a black box classifier.
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