XAI Evaluation Cards: A Practical Method for Designing Human-Centred XAI Evaluations

📅 2026-10-01
📈 Citations: 0
✨ Influential: 0
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🤖 AI Summary
This study addresses the fragmented dimension selection, lack of systematicity, and insufficient human-centered perspectives in Explainable Artificial Intelligence (XAI) evaluation by constructing a systematic assessment framework grounded in a bibliometric analysis of 82 studies. The core innovation lies in developing a card-sorting method comprising 36 cards that concretizes abstract evaluation dimensions, thereby assisting researchers in efficiently designing human-centered XAI evaluation procedures. Empirically validated through two groups of researchers, this approach effectively streamlines the evaluation design process, facilitates multidisciplinary collaboration, and significantly enhances both the comprehensiveness and structural rigor of XAI system assessments.
📝 Abstract
Evaluating explainable AI (XAI) systems from a human-centred approach requires researchers to select from numerous evaluation dimensions and measures, often in an ad hoc and fragmented manner. This paper introduces a method to help HCI, computer science, designers and social science researchers systematically evaluate XAI systems. The approach is based on an updated XAI-specific evaluation framework derived from an analysis of 82 studies. Using this framework, we developed a card-sorting method with 36 cards to help researchers prioritise relevant evaluation aspects. The process was tested with two research groups (n = 13) across five projects. The XAI Evaluation Cards are available as a printable appendix, along with an online repository of methods from previous XAI studies. Although not exhaustive, our findings indicate that the card-sorting approach can organise and streamline the design of the evaluation process, encouraging a more comprehensive and multidisciplinary assessment of XAI systems in research and development.
Problem

Research questions and friction points this paper is trying to address.

Explainable AI
Human-centred evaluation
Evaluation dimensions
XAI assessment
Innovation

Methods, ideas, or system contributions that make the work stand out.

Explainable AI (XAI)
Human-Centred Evaluation
Card-Sorting Method
Evaluation Framework
Multidisciplinary Assessment
K
Kristýna Sirka Kacafírková
imec-SMIT, Vrije Universiteit Brussel, Belgium
I
Ivania Donoso-Guzmán
KU Leuven, Department of Computer Science, Belgium and Augment, imec research group at KU Leuven, Belgium and Computer Science Department, Pontificia Universidad Católica de Chile, Chile
Denis Parra
Denis Parra
Pontificia Universidad Católica de Chile
Recommender SystemsIntelligent User InterfacesInformation VisualizationArtificial IntelligenceMedical AI
Katrien Verbert
Katrien Verbert
Computer Science Department
human-centered AIuser modelingexplainable AI
A
An Jacobs
imec-SMIT, Vrije Universiteit Brussel, Belgium