Ratti, Emanuele (2025) Epistemic Control and the Normativity of Machine Learning-Based Science. [Preprint]

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Abstract

The past few years have witnessed an increasing use of machine learning (ML) tools in science. Paul Humphreys has argued that, because of specific characteristics of ML systems, human scientists are pushed out-of-the-loop of science. In this chapter, I investigate to what extent this is true. First, I express these concerns in terms of what I call ‘epistemic control’. I identify two conditions for epistemic control, which I call ‘tracking’ and ‘tracing’, drawing on works in philosophy of technology. With this new understanding of the problem, I then argue against Humphreys’ pessimistic view. Finally, I construct a more nuanced view of epistemic control in ML-based science.

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Item Type:	Preprint
Creators:	Creators Email ORCID Ratti, Emanuele mnl.ratti@gmail.com 0000-0003-1409-8240
Additional Information:	Chapter to be published in "The Role of AI in Science: Epistemological and Methodological Studies" (Routledge), edited by David Barack, André Curtis-Trudel, and Darrell Rowbottom
Keywords:	machine learning; epistemic control; cognitive values; normativity
Subjects:	General Issues > Data Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Models and Idealization General Issues > Values In Science
Depositing User:	Dr Emanuele Ratti
Date Deposited:	16 Jan 2026 13:48
Last Modified:	16 Jan 2026 13:48
Item ID:	27926
Subjects:	General Issues > Data Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Models and Idealization General Issues > Values In Science
Date:	2025
URI:	https://philsci-archive.pitt.edu/id/eprint/27926

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• Epistemic Control and the Normativity of Machine Learning-Based Science. (deposited 22 Aug 2025 14:36)
  • Epistemic Control and the Normativity of Machine Learning-Based Science. (deposited 16 Jan 2026 13:48) [Currently Displayed]

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