Synthese 198 (2):1807-1827 (forthcoming)

Authors
Arno Schubbach
University of Basel
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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Reprint years 2019
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DOI 10.1007/s11229-019-02167-z
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References found in this work BETA

Critique of Pure Reason.I. Kant - 1787/1998 - Philosophy 59 (230):555-557.
Minds, Brains and Science.John R. Searle - 1984 - Harvard University Press.
Critique of the Power of Judgment.Immanuel Kant - 2000 - Cambridge University Press.
On the Proper Treatment of Connectionism.Paul Smolensky - 1988 - Behavioral and Brain Sciences 11 (1):1-23.

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Citations of this work BETA

A Puzzle concerning Compositionality in Machines.Ryan M. Nefdt - 2020 - Minds and Machines 30 (1):47-75.
The State Space of Artificial Intelligence.Holger Lyre - 2020 - Minds and Machines 30 (3):325-347.

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