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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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Communications of the ACM, 2022 Opening the black box or Pandora's Box?
Veda C. Storey +3 more
core +10 more sources
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
doaj +2 more sources
Explainable AI needs formalization [PDF]
npj Artificial IntelligenceThe field of “explainable artificial intelligence” (XAI) seemingly addresses the desire that decisions of machine learning systems should be human-understandable. However, in its current state, XAI itself needs scrutiny.
Stefan Haufe +6 more
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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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How to Make AlphaGo’s Children Explainable
Philosophies, 2022 Under the rubric of understanding the problem of explainability of AI in terms of abductive cognition, I propose to review the lessons from AlphaGo and her more powerful successors.
Woosuk Park
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Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI
CoRR, 2021 Many ML models are opaque to humans, producing decisions too complex for humans to easily understand. In response, explainable artificial intelligence (XAI) tools that analyze the inner workings of a model have been created. Despite these tools' strength in translating model behavior, critiques have raised concerns about the impact of XAI tools as a ...
Kiana Alikhademi +3 more
openaire +2 more sources
The false hope of current approaches to explainable artificial intelligence in health care
The Lancet: Digital Health, 2021 Summary: The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable to be used in high-stakes scenarios such as medicine.
Marzyeh Ghassemi, PhD +2 more
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From ”Explainable AI” to ”Graspable AI”
Proceedings of the Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction, 2021 Since the advent of Artificial Intelligence (AI) and Machine Learning (ML), researchers have asked how intelligent computing systems could interact with and relate to their users and their surroundings, leading to debates around issues of biased AI systems, ML black-box, user trust, user’s perception of control over the system, and system’s ...
Maliheh Ghajargar +7 more
openaire +6 more sources
A Review of Trustworthy and Explainable Artificial Intelligence (XAI)
IEEE Access, 2023 The advancement of Artificial Intelligence (AI) technology has accelerated the development of several systems that are elicited from it. This boom has made the systems vulnerable to security attacks and allows considerable bias in order to handle errors ...
Vinay Chamola +5 more
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