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  1. (2 other versions)An Enquiry Concerning Human Understanding.David Hume - 1901 - The Monist 11:312.
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  • (5 other versions)Enquiry Concerning Human Understanding.David Hume (ed.) - 1904 - Clarendon Press.
    Oxford Philosophical Texts Series Editor: John Cottingham The Oxford Philosophical Texts series consists of authoritative teaching editions of canonical texts in the history of philosophy from the ancient world down to modern times. Each volume provides a clear, well laid out text together with a comprehensive introduction by a leading specialist, giving the student detailed critical guidance on the intellectual context of the work and the structure and philosophical importance of the main arguments. Endnotes are supplied which provide further commentary (...)
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  • (2 other versions)An enquiry concerning human understanding.David Hume - 2000 - In Steven M. Cahn (ed.), Exploring Philosophy: An Introductory Anthology. New York, NY, United States of America: Oxford University Press USA. pp. 112.
    David Hume's Enquiry concerning Human Understanding is the definitive statement of the greatest philosopher in the English language. His arguments in support of reasoning from experience, and against the "sophistry and illusion"of religiously inspired philosophical fantasies, caused controversy in the eighteenth century and are strikingly relevant today, when faith and science continue to clash. The Enquiry considers the origin and processes of human thought, reaching the stark conclusion that we can have no ultimate understanding of the physical world, or indeed (...)
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  • Dermatologist-level classification of skin cancer with deep neural networks.Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau & Sebastian Thrun - 2017 - Nature 542 (7639):115-118.
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  • Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Cynthia Rudin - 2019 - Nature Machine Intelligence 1.
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  • How the machine ‘thinks’: Understanding opacity in machine learning algorithms.Jenna Burrell - 2016 - Big Data and Society 3 (1):205395171562251.
    This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: opacity as intentional corporate or state (...)
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  • Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation.Sandra Wachter, Brent Mittelstadt & Luciano Floridi - 2017 - International Data Privacy Law 1 (2):76-99.
    Since approval of the EU General Data Protection Regulation (GDPR) in 2016, it has been widely and repeatedly claimed that the GDPR will legally mandate a ‘right to explanation’ of all decisions made by automated or artificially intelligent algorithmic systems. This right to explanation is viewed as an ideal mechanism to enhance the accountability and transparency of automated decision-making. However, there are several reasons to doubt both the legal existence and the feasibility of such a right. In contrast to the (...)
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  • Explanation in artificial intelligence: Insights from the social sciences.Tim Miller - 2019 - Artificial Intelligence 267 (C):1-38.
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  • On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation.Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller & Wojciech Samek - 2015 - PLoS ONE 10 (7):e0130140.
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  • Unmasking Clever Hans Predictors and Assessing What Machines Really Learn.Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek & Klaus-Robert Müller - 2019 - Nature Communications 10 (1):1--8.
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