Abstract
Explainable artificial intelligence (XAI) has gained increasing attention in the medical field, where understanding the reasons for predictions is crucial. In this paper we introduce an interactive and dynamic visual interface providing global, local and counterfactual explanations to end-users, with a use case in healthcare. The dataset used in the study is about predicting an individual’s coronary heart disease (CHD) within 10 years using the decision tree classification method. We evaluated our XAI system with 200 participants. Our results show that the participants reported an overall good assessment of the user interface, with non-expert users showing a higher satisfaction than users who have some degree of knoweldge of AI.
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This work is partially funded by the EPSRC CHAI project (EP/T026820/1)
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Hu, J., Liang, Y., Zhao, W., McAreavey, K., Liu, W. (2023). An Interactive XAI Interface with Application in Healthcare for Non-experts. In: Longo, L. (eds) Explainable Artificial Intelligence. xAI 2023. Communications in Computer and Information Science, vol 1901. Springer, Cham. https://doi.org/10.1007/978-3-031-44064-9_35
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