Synthese:1-21 (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.
Keywords No keywords specified (fix it)
Categories No categories specified
(categorize this paper)
DOI 10.1007/s11229-019-02167-z
Options
Edit this record
Mark as duplicate
Export citation
Find it on Scholar
Request removal from index
Revision history

Download options

PhilArchive copy


Upload a copy of this paper     Check publisher's policy     Papers currently archived: 52,768
Through your library

References found in this work BETA

No references found.

Add more references

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 - forthcoming - Minds and Machines:1-23.

Add more citations

Similar books and articles

Where Do Features Come From?Geoffrey Hinton - 2014 - Cognitive Science 38 (6):1078-1101.
Inductive Learning by Machines.Stuart Russell - 1991 - Philosophical Studies 64 (October):37-64.
Atomistic Learning in Non-Modular Systems.Pierre Poirier - 2005 - Philosophical Psychology 18 (3):313-325.
Varieties of Justification in Machine Learning.David Corfield - 2010 - Minds and Machines 20 (2):291-301.

Analytics

Added to PP index
2019-03-05

Total views
69 ( #136,251 of 2,341,541 )

Recent downloads (6 months)
16 ( #39,717 of 2,341,541 )

How can I increase my downloads?

Downloads

My notes