About: A multi-component framework for the analysis and design of explainable artificial intelligence

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	A multi-component framework for the analysis and design of explainable artificial intelligence
Creator	Atakishiyev, S Babiker, H Farruque, N Goebel, R Kim, M-Y Motallebi, M Rabelo, J Syed, T Zaïane, O
source	ArXiv
abstract	The rapid growth of research in explainable artificial intelligence (XAI) follows on two substantial developments. First, the enormous application success of modern machine learning methods, especially deep and reinforcement learning, which have created high expectations for industrial, commercial and social value. Second, the emergence of concern for creating trusted AI systems, including the creation of regulatory principles to ensure transparency and trust of AI systems.These two threads have created a kind of%22perfect storm%22of research activity, all eager to create and deliver it any set of tools and techniques to address the XAI demand. As some surveys of current XAI suggest, there is yet to appear a principled framework that respects the literature of explainability in the history of science, and which provides a basis for the development of a framework for transparent XAI. Here we intend to provide a strategic inventory of XAI requirements, demonstrate their connection to a history of XAI ideas, and synthesize those ideas into a simple framework to calibrate five successive levels of XAI.
has issue date	2020-05-05(xsd:dateTime)
has license	arxiv
sha1sum (hex)	1ea26f19d99cbc108530aeb18c636762b882e1ef
resource representing a document's title	A multi-component framework for the analysis and design of explainable artificial intelligence
resource representing a document's body	covid:1ea26f19d99cbc108530aeb18c636762b882e1ef#body_text
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