Streppel, Yeji (2024) Demarcating value demarcation in ML. In: UNSPECIFIED.

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Abstract

It has become widely recognized that machine learning (ML) systems are value-laden. This raises a value demarcation problem: how can we distinguish between legitimate and illegitimate non-epistemic value influences in ML development and use? This paper makes two contributions. First, it surveys value demarcation strategies in ML and identifies gaps in the debate. Second, it addresses a deeper issue: what makes for a good demarcation strategy? We need a way to judge the adequacy of existing demarcation strategies across contexts. I submit contextual adequacy as a meta-norm for evaluating the prima facie justification of value demarcation proposals in ML.

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Item Type:	Conference or Workshop Item (UNSPECIFIED)
Creators:	Creators Email ORCID Streppel, Yeji y.j.m.b.k.streppel@tue.nl 0009-0002-6151-8009
Keywords:	non-epistemic values, value demarcation, meta-norms, machine learning
Subjects:	Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Values In Science
Depositing User:	Ms Yeji Streppel
Date Deposited:	05 Sep 2024 12:50
Last Modified:	05 Sep 2024 12:50
Item ID:	23875
Subjects:	Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Values In Science
Date:	March 2024
URI:	https://philsci-archive.pitt.edu/id/eprint/23875

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