Epistemic Control and the Normativity of Machine Learning-Based Science

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1. Verfasser: Ratti, Emanuele
Format: Preprint
Veröffentlicht: 2026
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author Ratti, Emanuele
author_facet Ratti, Emanuele
contents The past few years have witnessed an increasing use of machine learning (ML) systems 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, called 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.
format Preprint
id arxiv_https___arxiv_org_abs_2601_11202
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Epistemic Control and the Normativity of Machine Learning-Based Science
Ratti, Emanuele
Computers and Society
Artificial Intelligence
The past few years have witnessed an increasing use of machine learning (ML) systems 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, called 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.
title Epistemic Control and the Normativity of Machine Learning-Based Science
topic Computers and Society
Artificial Intelligence
url https://arxiv.org/abs/2601.11202