Machine learning and essentialism

Machine learning and essentialism have been connected in the past by various researchers, in order to state that the main paradigm in machine learning processes is equivalent to choosing the “essential” attributes for the machine to search for. Our goal in this paper is to show that there are connec...

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Bibliographic Details
Published inZagadnienia Filozoficzne w Nauce (Online) Vol. 73; no. 73; pp. 171 - 196
Main Authors Šekrst, Kristina, Skansi, Sandro
Format Journal Article
LanguageEnglish
German
Published Copernicus Center Press 01.01.2022
Subjects
accidental properties
essentialism
machine learning
modal necessity
pattern recognition
Philosophy
similarity-based approach
Special Branches of Philosophy
modal necessity
accidental properties
machine learning
similaritybased approach
essentialism
pattern recognition
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Summary:Machine learning and essentialism have been connected in the past by various researchers, in order to state that the main paradigm in machine learning processes is equivalent to choosing the “essential” attributes for the machine to search for. Our goal in this paper is to show that there are connections between machine learning and essentialism, but only for some kinds of machine learning, and often not including deep learning methods. Similarity-based approaches, more connected to the overall prototype theory, spanning from psychology and linguistics, seem more suited for pattern recognition and complex deep-learning issues, while for classification problems, mostly for unsupervised learning, essentialism seems like the best choice. In order to illustrate the difference better, we will connect both paths to their sources in other disciplines and see how human psychology influences our decision in machine-learning modeling as well. This leads to a philosophically very interesting consequence: even in the setting of supervised machine learning, essences are not present in data, but in targets, which in turn means that the categories which purport to be essences are in fact human-made, and hand-coded in the targets. The success of machine learning, therefore, does not give any substantial evidence for the independent existence of essential properties. Our stance here is to state that neither the existence nor the lack of “essential” properties in machine learning can lead to metaphysical, i.e., ontological claims.
ISSN:0867-8286
2451-0602