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This paper reports two experiments (N=349) on the impact of post hocexplanations by example and error rates on peoples perceptions of a black boxclassifier. Both experiments show that when people are given case basedexplanations, from an implemented ANN CBR twin system, they perceive missclassifications to be more correct. They also show that as error rates increaseabove 4%, people trust the classifier less and view it as being less correct,less reasonable and less trustworthy. The implications of these results for XAIare discussed.

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