Sterkenburg, Tom F. (2025) Values in machine learning: What follows from underdetermination? [Preprint]

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Abstract

It has been argued that inductive underdetermination entails that machine learning algorithms must be value-laden. This paper draws from the philosophy of induction to rather highlight the epistemic motivations and justifications that play a role in machine learning algorithm design. The analysis offered indicates that some of the arguments from underdetermination to value-ladenness are too quick, but it also supports their conclusion by indicating how the practical realization of these epistemic considerations inevitably introduces various non-epistemically value-laden judgments, too. The suggestion is that exposing value-ladenness is not inconsistent with, and even profits from, appreciation of the epistemic considerations involved.

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Item Type:	Preprint
Creators:	Creators Email ORCID Sterkenburg, Tom F. tom.sterkenburg@lmu.de 0000-0002-4860-727X
Subjects:	General Issues > Confirmation/Induction Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Values In Science
Depositing User:	Mr Tom Sterkenburg
Date Deposited:	20 Dec 2025 13:40
Last Modified:	20 Dec 2025 13:40
Item ID:	27570
Subjects:	General Issues > Confirmation/Induction Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Values In Science
Date:	2025
URI:	https://philsci-archive.pitt.edu/id/eprint/27570

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• Values in machine learning: What follows from underdetermination? (deposited 20 Dec 2024 14:42)
  • Values in machine learning: What follows from underdetermination? (deposited 20 Dec 2025 13:40) [Currently Displayed]

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