# Explaining Classifications to Non Experts: An XAI User Study of Post Hoc Explanations for a Classifier When People Lack Expertise

arXiv: https://arxiv.org/abs/2212.09342 · HTML: https://mlsanity.com/paper/2212.09342/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 19 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients](https://mlsanity.com/paper/1711.09404/) (2017)
- [If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques](https://mlsanity.com/paper/2103.01035/) (2021)
- [The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems](https://mlsanity.com/paper/2008.09100/) (2020)
- [An Ensemble of Simple Convolutional Neural Network Models for MNIST Digit Recognition](https://mlsanity.com/paper/2008.10400/) (2020)
- [Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI](https://mlsanity.com/paper/2204.10152/) (2022)
- [Improving Human-Machine Cooperative Visual Search With Soft Highlighting](https://mlsanity.com/paper/1612.08117/) (2016)

## Built on by (most cited first, 8 of 8)

- [The Impact of Imperfect XAI on Human-AI Decision-Making](https://mlsanity.com/paper/2307.13566/) (2023)
- [Allowing humans to interactively guide machines where to look does not always improve human-AI team's classification accuracy](https://mlsanity.com/paper/2404.05238/) (2024)
- [Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisions](https://mlsanity.com/paper/2505.01192/) (2025)
- [Beyond Satisfaction: From Placebic to Actionable Explanations For Enhanced Understandability](https://mlsanity.com/paper/2512.06591/) (2025)
- [Mean Opinion Score as a New Metric for User-Evaluation of XAI Methods](https://mlsanity.com/paper/2407.20427/) (2024)
- [Explainable few-shot learning workflow for detecting invasive and exotic tree species](https://mlsanity.com/paper/2411.00684/) (2024)
- [Dataset resulting from the user study on comprehensibility of explainable AI algorithms](https://mlsanity.com/paper/2411.02419/) (2024)
- [Feature-Guided Neighbor Selection for Non-Expert Evaluation of Model Predictions](https://mlsanity.com/paper/2507.06029/) (2025)

## Abstract (arXiv)

Very few eXplainable AI (XAI) studies consider how users understanding of explanations might change depending on whether they know more or less about the to be explained domain (i.e., whether they differ in their expertise). Yet, expertise is a critical facet of most high stakes, human decision making (e.g., understanding how a trainee doctor differs from an experienced consultant). Accordingly, this paper reports a novel, user study (N=96) on how peoples expertise in a domain affects their understanding of post-hoc explanations by example for a deep-learning, black box classifier. The results show that peoples understanding of explanations for correct and incorrect classifications changes dramatically, on several dimensions (e.g., response times, perceptions of correctness and helpfulness), when the image-based domain considered is familiar (i.e., MNIST) as opposed to unfamiliar (i.e., Kannada MNIST). The wider implications of these new findings for XAI strategies are discussed.