# Painting the black box white: experimental findings from applying XAI to an ECG reading setting

arXiv: https://arxiv.org/abs/2210.15236 · HTML: https://mlsanity.com/paper/2210.15236/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 85 references that are in the map.
- First posted on arXiv 27 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey](https://mlsanity.com/paper/2104.00950/) (2021)
- [The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems](https://mlsanity.com/paper/2105.03354/) (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)
- [The Utility of Explainable AI in Ad Hoc Human-Machine Teaming](https://mlsanity.com/paper/2209.03943/) (2022)
- [Reviewing the Need for Explainable Artificial Intelligence (xAI)](https://mlsanity.com/paper/2012.01007/) (2020)
- [Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI](https://mlsanity.com/paper/2105.12497/) (2021)
- [Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making](https://mlsanity.com/paper/2005.10960/) (2020)
- [Principles of Explanation in Human-AI Systems](https://mlsanity.com/paper/2102.04972/) (2021)
- [On the Influence of Explainable AI on Automation Bias](https://mlsanity.com/paper/2204.08859/) (2022)
- [Dice in the Black Box: User Experiences with an Inscrutable Algorithm](https://mlsanity.com/paper/1812.03219/) (2018)
- [Tree-based local explanations of machine learning model predictions, AraucanaXAI](https://mlsanity.com/paper/2110.08272/) (2021)

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

- [Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review](https://mlsanity.com/paper/2310.19778/) (2023)
- [Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration](https://mlsanity.com/paper/2409.12809/) (2024)
- [Beyond Explainability: The Case for AI Validation](https://mlsanity.com/paper/2505.21570/) (2025)
- [Who Benefits from AI Explanations? Towards Accessible and Interpretable Systems](https://mlsanity.com/paper/2508.10806/) (2025)

## Abstract (arXiv)

The shift from symbolic AI systems to black-box, sub-symbolic, and statistical ones has motivated a rapid increase in the interest toward explainable AI (XAI), i.e. approaches to make black-box AI systems explainable to human decision makers with the aim of making these systems more acceptable and more usable tools and supports. However, we make the point that, rather than always making black boxes transparent, these approaches are at risk of painting the black boxes white, thus failing to provide a level of transparency that would increase the system's usability and comprehensibility; or, even, at risk of generating new errors, in what we termed the white-box paradox. To address these usability-related issues, in this work we focus on the cognitive dimension of users' perception of explanations and XAI systems. To this aim, we designed and conducted a questionnaire-based experiment by which we involved 44 cardiology residents and specialists in an AI-supported ECG reading task. In doing so, we investigated different research questions concerning the relationship between users' characteristics (e.g. expertise) and their perception of AI and XAI systems, including their trust, the perceived explanations' quality and their tendency to defer the decision process to automation (i.e. technology dominance), as well as the mutual relationships among these different dimensions. Our findings provide a contribution to the evaluation of AI-based support systems from a Human-AI interaction-oriented perspective and lay the ground for further investigation of XAI and its effects on decision making and user experience.