mlsanityData to 14 Oct 2026

WebXAII: an open-source web framework to study human-XAI interaction

arXiv 2506.14777 ↗First posted 16 May 2025cs.HC cs.AI cs.LGcited by 0

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Abstract (arXiv)

This article introduces WebXAII, an open-source web framework designed to facilitate research on human interaction with eXplainable Artificial Intelligence (XAI) systems. The field of XAI is rapidly expanding, driven by the growing societal implications of the widespread adoption of AI (and in particular machine learning) across diverse applications. Researchers who study the interaction between humans and XAI techniques typically develop ad hoc interfaces in order to conduct their studies. These interfaces are usually not shared alongside the results of the studies, which limits their reusability and the reproducibility of experiments. In response, we design and implement WebXAII, a web-based platform that can embody full experimental protocols, meaning that it can present all aspects of the experiment to human participants and record their responses. The experimental protocols are translated into a composite architecture of generic views and modules, which offers a lot of flexibility. The architecture is defined in a structured configuration file, so that protocols can be implemented with minimal programming skills. We demonstrate that WebXAII can effectively embody relevant protocols, by reproducing the protocol of a state-of-the-art study of the literature.

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References · 23

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11 more
  1. 2019Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networkscited by 582
  2. 2020A Survey on Neural Network Interpretabilitycited by 291
  3. 2022From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagationcited by 141
  4. 2022Explanations Can Reduce Overreliance on AI Systems During Decision-Makingcited by 111
  5. 2021What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methodscited by 98
  6. 2023In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Makingcited by 50
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  8. 2024Token Transformation Matters: Towards Faithful Post-hoc Explanation for Vision Transformercited by 19
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  10. 2024How should AI decisions be explained? Requirements for Explanations from the Perspective of European Lawcited by 11
  11. 2024How explainable AI affects human performance: A systematic review of the behavioural consequences of saliency mapscited by 6

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