探讨机器学习如何影响科学中的认知控制，提出更平衡的应对思路。
Epistemic Control and the Normativity of Machine Learning-Based Science
- 用追踪与追溯能力定义科学中的认知控制
- 反驳机器学习使科学家被边缘化的悲观观点
- 为科研人员在ML时代保持主导权提供新框架
近年来，机器学习系统在科学研究中应用日益广泛。保罗·赫姆弗雷斯认为，由于机器学习系统的特定属性，人类科学家正被排除在科学过程之外。本文探讨这一观点的成立程度。首先，我以‘认知控制’这一概念重新表述这些担忧，提出两个关键条件：追踪与追溯，借鉴技术哲学相关研究。在此基础上，我反对赫姆弗雷斯的悲观立场，并构建一个关于机器学习科学中认知控制的更细致、更平衡的理解。
原文摘要 · Abstract (English)
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.
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