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.

翻译：过去几年，机器学习系统在科学领域的应用日益广泛。保罗·汉弗莱斯认为，由于机器学习系统的特定特性，人类科学家正被排除在科学循环之外。本章将探讨这一论断在多大程度上成立。首先，我将这些关切表述为我称之为"认识论控制"的概念。借鉴技术哲学的相关研究，我提出了认识论控制的两个条件：追踪与溯因。基于对这一问题的全新理解，我进而反驳汉弗莱斯的悲观观点。最后，我构建了一个更为精细的基于机器学习的科学认识论控制框架。