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Judging Machines. Philosophical Aspects of Deep Learning

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Schubbach Arno,
Project Begriffe und Praktiken der Darstellung in Philosophie, Chemie und Malerei um 1800
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Original article (peer-reviewed)

Journal Synthese An International Journal for Epistemology, Methodology and Philosophy of Science
Title of proceedings Synthese An International Journal for Epistemology, Methodology and Philosophy of Science

Open Access

URL http://philsci-archive.pitt.edu/15780/
Type of Open Access Repository (Green Open Access)

Abstract

Although machine learning has been successful in recent years and is increasingly being deployed in the sciences, enterprises or administrations, it has rarely been discussed in philosophy beyond the philosophy of mathematics and machine learning. The present contribution addresses the resulting lack of conceptual tools for an epistemological discussion of machine learning by conceiving of deep learning networks as 'judging machines' and using the Kantian analysis of judgments for specifying the type of judgment they are capable of. At the center of the argument is the fact that the functionality of deep learning networks is established by training and cannot be explained and justified by reference to a predefined rule-based procedure. Instead, the computational process of a deep learning network is barely explainable and needs further justification, as is shown in reference to the current research literature. Thus, it requires a new form of justification, that is to be specified with the help of Kant's epistemology.
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