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Why we do need explainable AI for healthcare [PDF]
Diagnostic and Prognostic ResearchThe recent uptake in certified Artificial Intelligence (AI) tools for healthcare applications has renewed the debate around their adoption. Explainable AI, the sub-discipline promising to render AI devices more transparent and trustworthy, has also come ...
Giovanni Cinà +3 more
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Explainable AI in healthcare: to explain, to predict, or to describe? [PDF]
Diagnostic and Prognostic ResearchExplainable Artificial Intelligence (AI) methods are designed to provide information about how AI-based models make predictions. In healthcare, there is a widespread expectation that these methods will provide relevant and accurate information about a ...
Alex Carriero +6 more
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Explainable AI improves task performance in human–AI collaboration [PDF]
Scientific ReportsArtificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human–AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains ...
Julian Senoner +4 more
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Explainable AI and echo state networks calibrate trust in human machine interaction [PDF]
Scientific ReportsTrust in human-machine interaction is a critical factor for the performance of AI systems, but achieving it is still challenging because many AI models are considered black-box.
Sijia Hao +5 more
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How Explainable Really Is AI? Benchmarking Explainable AI
LogicsThis work contextualizes the possibility of deriving a unifying artificial intelligence framework by walking in the footsteps of General, Explainable, and Verified Artificial Intelligence (GEVAI): by considering explainability not only at the level of ...
Giacomo Bergami, Oliver Robert Fox
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The effectiveness of explainable AI on human factors in trust models [PDF]
Scientific ReportsExplainable AI has garnered significant traction in science communication research. Prior empirical studies have firmly established that explainable AI communication could improve trust in AI and that trust in AI engineers was argued to be an under ...
Justin C. Cheung, Shirley S. Ho
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Explainable artificial intelligence in emergency medicine: an overview [PDF]
Clinical and Experimental Emergency Medicine, 2023 Artificial intelligence (AI) and machine learning (ML) have potential to revolutionize emergency medical care by enhancing triage systems, improving diagnostic accuracy, refining prognostication, and optimizing various aspects of clinical care.
Yohei Okada +2 more
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Communications of the ACM, 2022 Opening the black box or Pandora's Box?
KC Santosh, Casey Wall
+7 more sources
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI [PDF]
ACM Computing Surveys, 2022 The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes raised the question of how to evaluate explanations of machine learning (ML) models.
Meike Nauta +8 more
semanticscholar +1 more source
Questioning the AI: Informing Design Practices for Explainable AI User Experiences [PDF]
International Conference on Human Factors in Computing Systems, 2020 A surge of interest in explainable AI (XAI) has led to a vast collection of algorithmic work on the topic. While many recognize the necessity to incorporate explainability features in AI systems, how to address real-world user needs for understanding AI ...
Q. Liao, D. Gruen, Sarah Miller
semanticscholar +1 more source