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Author
Date
2021-02Type
- Journal Article
Citations
Cited 10 times in
Web of Science
Cited 14 times in
Scopus
ETH Bibliography
yes
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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. Show more
Publication status
publishedExternal links
Journal / series
SyntheseVolume
Pages / Article No.
Publisher
SpringerSubject
Deep learning; Machine learning; Artificial intelligence; Algorithm; Computation; Judgment; Explanation; Justification; KantOrganisational unit
03665 - Hampe, Michael / Hampe, Michael
Funding
165574 - Begriffe und Praktiken der Darstellung in Philosophie, Chemie und Malerei um 1800 (SNF)
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Show all metadata
Citations
Cited 10 times in
Web of Science
Cited 14 times in
Scopus
ETH Bibliography
yes
Altmetrics