Ratti, Emanuele (2024) Machine Learning and the Ethics of Induction. [Preprint]

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Abstract

This chapter analyzes the inferential structure of machine learning (ML) systems, and shows how these can be value-laden in unexpected ways. ML systems follow an inductive inferential strategy, which is based on two components. First, there is the basic assumption that we are entitled to predict future events on the basis of past occurrences because the world will not drastically change. This assumption is called ‘uniformity of nature’ (UoN). Second, ‘canons of inductive inference’ (CIIs) are required to narrow down the set of possible hypotheses that one can generate from UoN. Debates on the ethics of ML have focused on CIIs. Here I show that UoN plays an important ethical role, in particular in eroding human agency.

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Item Type:	Preprint
Creators:	Creators Email ORCID Ratti, Emanuele mnl.ratti@gmail.com 0000-0003-1409-8240
Additional Information:	Forthcoming in "Philosophy of Science for Machine Learning: Core Issues and New Perspectives", edited by Juan Duran and Giorgia Pozzi, Synthese Library, Springer
Keywords:	machine learning; induction; ethics of AI; AI ethics
Subjects:	Specific Sciences > Artificial Intelligence > AI and Ethics Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Science and Society General Issues > Technology General Issues > Values In Science
Depositing User:	Dr Emanuele Ratti
Date Deposited:	05 Apr 2024 06:36
Last Modified:	05 Apr 2024 06:36
Item ID:	23260
Subjects:	Specific Sciences > Artificial Intelligence > AI and Ethics Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Science and Society General Issues > Technology General Issues > Values In Science
Date:	2024
URI:	https://philsci-archive.pitt.edu/id/eprint/23260

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