A comprehensive assessment of the impact of eXplainable AI (XAI) on diagnostic decision-making should adopt a socio-technical perspective. Our study focuses on Decision Support Systems (DSS) that provide explanations in the form of Activation Maps, assessing their impact in terms of automation bias and algorithmic aversion. Specifically, we focus on the XAI-assisted task of detecting thoraco-lumbar fractures from X-rays by radiologists, taking into account the complexity of the cases and the experience level of users. Our results show how XAI support has a clear and positive impact on diagnostic performance. By introducing the concepts of technology impact, reliance patterns, and the white box paradox, we highlight the importance of designing Human-AI Collaboration Protocols (HAI-CP) that are specific to the task at hand to optimize the integration of XAI into diagnostic decision-making.
Natali, C., Famiglini, L., Campagner, A., La Maida, G., Gallazzi, E., Cabitza, F. (2023). Color Shadows 2: Assessing the Impact of XAI on Diagnostic Decision-Making. In Explainable Artificial Intelligence First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part I (pp.618-629). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-44064-9_33].
Color Shadows 2: Assessing the Impact of XAI on Diagnostic Decision-Making
Natali C.
;Famiglini L.;Campagner A.;Cabitza F.
2023
Abstract
A comprehensive assessment of the impact of eXplainable AI (XAI) on diagnostic decision-making should adopt a socio-technical perspective. Our study focuses on Decision Support Systems (DSS) that provide explanations in the form of Activation Maps, assessing their impact in terms of automation bias and algorithmic aversion. Specifically, we focus on the XAI-assisted task of detecting thoraco-lumbar fractures from X-rays by radiologists, taking into account the complexity of the cases and the experience level of users. Our results show how XAI support has a clear and positive impact on diagnostic performance. By introducing the concepts of technology impact, reliance patterns, and the white box paradox, we highlight the importance of designing Human-AI Collaboration Protocols (HAI-CP) that are specific to the task at hand to optimize the integration of XAI into diagnostic decision-making.
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