Judging machines: philosophical aspects of deep learning.
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 lac...
| Publicado en: | Synthese Vol. 198; no. 2; pp. 1807 - 1828 |
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| Formato: | Artículo |
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Springer Nature
Feb2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=149024557&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 149024557 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Feb2021 vid: 198 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 149024557 10.1007/s11229-019-02167-z ppf: 1807 ppct: 21 formats: fmt: @attributes: type: P size: 331KB tig: atl: Judging machines: philosophical aspects of deep learning. aug: au: Schubbach, Arno affil: Department of Humanities, Social and Political Sciences, ETH Zurich, Zurich, Switzerland su: Machine learning Philosophy of mathematics Artificial intelligence Deep learning sug: subj: Machine learning Philosophy of mathematics Artificial intelligence Deep learning keyword: Algorithm Computation Explanation Judgment Justification Kant ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2021. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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