Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies

•Series of controlled user studies examining post-hoc example-based explanations for black-box deep learners doing classification (XAI).•Black box AI models can be explained by “twinning” them with white-box models.•Explanations were only found to impact people’s perception of errors.•Explanations lead people to view errors as being “less incorrect”, but they do not improve trust.•Trust in an AI model is undermined by increases in error-rates (from 3% error-levels onwards).