# 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

arXiv: https://arxiv.org/abs/2009.06349 · HTML: https://mlsanity.com/paper/2009.06349/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments](https://mlsanity.com/paper/2212.07425/) (2022)
- [Case-Based Reasoning with Language Models for Classification of Logical Fallacies](https://mlsanity.com/paper/2301.11879/) (2023)
- [Multimodal and Explainable Internet Meme Classification](https://mlsanity.com/paper/2212.05612/) (2022)
- [Cultural Bias in Explainable AI Research: A Systematic Analysis](https://mlsanity.com/paper/2403.05579/) (2024)

## Abstract (arXiv)

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.