Epistemic Control and the Normativity of Machine Learning-Based Science

TL;DR

This paper examines whether machine learning systems diminish human scientists' epistemic control in science, arguing against pessimistic views and proposing a nuanced understanding of control conditions like tracking and tracing.

Contribution

It introduces the concepts of tracking and tracing as conditions for epistemic control and offers a nuanced perspective on the role of ML in scientific epistemology.

Findings

Humans can maintain epistemic control through tracking and tracing.

ML systems do not necessarily push scientists out of the scientific process.

A more nuanced view of epistemic control in ML-based science is proposed.

Abstract

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.