Towards an Evaluation Framework for Explainable Artificial Intelligence Systems for Health and Well-being
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Abstract (arXiv)
The integration of Artificial Intelligence in the development of computer systems presents a new challenge: make intelligent systems explainable to humans. This is especially vital in the field of health and well-being, where transparency in decision support systems enables healthcare professionals to understand and trust automated decisions and predictions. To address this need, tools are required to guide the development of explainable AI systems. In this paper, we introduce an evaluation framework designed to support the development of explainable AI systems for health and well-being. Additionally, we present a case study that illustrates the application of the framework in practice. We believe that our framework can serve as a valuable tool not only for developing explainable AI systems in healthcare but also for any AI system that has a significant impact on individuals.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 42 references that are in the map. First posted on arXiv 11 Apr 2025.
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18 of 42 references.
References · 18
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6 more
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