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. 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.Comment: 2 Figures, 1 Table, 8 page
