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Judging Machines. Philosophical Aspects of Deep Learning

Schubbach, Arno (2019) Judging Machines. Philosophical Aspects of Deep Learning. [Preprint]

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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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Item Type: Preprint
Creators:
CreatorsEmailORCID
Schubbach, Arnoa.schubbach@unibas.ch0000-0003-4536-3940
Additional Information: Accpeted for publication in "Synthese. An International Journal for Epistemology, Methodology and Philosophy of Science"
Keywords: deep learning; machine learning; artificial intelligence; Algorithm; Computation; Judgment; Explanation; justification; Kant
Subjects: General Issues > Data
Specific Sciences > Artificial Intelligence
General Issues > Explanation
General Issues > Technology
Depositing User: Dr. Arno Schubbach
Date Deposited: 02 Mar 2019 01:13
Last Modified: 02 Mar 2019 01:13
Item ID: 15780
Subjects: General Issues > Data
Specific Sciences > Artificial Intelligence
General Issues > Explanation
General Issues > Technology
Date: 2019
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/15780

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