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 people’s perceptions of a black-box classifier. Both experiments show that when people are given case-based explanations, from an implemented ANN-CBR twin system, they perceive miss-classifications to be more correct. They also show that as error rates increase above ~4%, people trust the classifier less and view it as being less correct, less reasonable and less trustworthy. The
implications of these results for XAI are discussed.