# Study on the Helpfulness of Explainable Artificial Intelligence

arXiv: https://arxiv.org/abs/2410.11896 · HTML: https://mlsanity.com/paper/2410.11896/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 44 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 19 of 19)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [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)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Unmasking Clever Hans Predictors and Assessing What Machines Really Learn](https://mlsanity.com/paper/1902.10178/) (2019)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Textual Explanations for Self-Driving Vehicles](https://mlsanity.com/paper/1807.11546/) (2018)
- [Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond](https://mlsanity.com/paper/2202.06861/) (2022)
- [From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation](https://mlsanity.com/paper/2206.03208/) (2022)
- [Model Complexity of Deep Learning: A Survey](https://mlsanity.com/paper/2103.05127/) (2021)
- [Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI](https://mlsanity.com/paper/2206.10847/) (2022)
- [What does LIME really see in images?](https://mlsanity.com/paper/2102.06307/) (2021)
- [Kandinsky Patterns](https://mlsanity.com/paper/1906.00657/) (2019)
- [CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations](https://mlsanity.com/paper/2204.02380/) (2022)
- [How much informative is your XAI? A decision-making assessment task to objectively measure the goodness of explanations](https://mlsanity.com/paper/2312.04379/) (2023)

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

- [Catch Me if You Search: When Contextual Web Search Results Affect the Detection of Hallucinations](https://mlsanity.com/paper/2504.01153/) (2025)
- [See What I Mean? CUE: A Cognitive Model of Understanding Explanations](https://mlsanity.com/paper/2506.14775/) (2025)
- [Autonomy by Design: Preserving Human Autonomy in AI Decision-Support](https://mlsanity.com/paper/2506.23952/) (2025)
- [Who Benefits from AI Explanations? Towards Accessible and Interpretable Systems](https://mlsanity.com/paper/2508.10806/) (2025)

## Abstract (arXiv)

Explainable Artificial Intelligence (XAI) is essential for building advanced machine learning-powered applications, especially in critical domains such as medical diagnostics or autonomous driving. Legal, business, and ethical requirements motivate using effective XAI, but the increasing number of different methods makes it challenging to pick the right ones. Further, as explanations are highly context-dependent, measuring the effectiveness of XAI methods without users can only reveal a limited amount of information, excluding human factors such as the ability to understand it. We propose to evaluate XAI methods via the user's ability to successfully perform a proxy task, designed such that a good performance is an indicator for the explanation to provide helpful information. In other words, we address the helpfulness of XAI for human decision-making. Further, a user study on state-of-the-art methods was conducted, showing differences in their ability to generate trust and skepticism and the ability to judge the rightfulness of an AI decision correctly. Based on the results, we highly recommend using and extending this approach for more objective-based human-centered user studies to measure XAI performance in an end-to-end fashion.