How Scientists Are Brought Back into Science—The Error of Empiricism

Mieke Boon*

*Corresponding author for this work

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Abstract

This chapter aims to contribute to critically investigate whether human-made scientific knowledge and the scientist’s role in developing it, will remain crucial—or can data-models automatically generated by machine-learning technologies replace scientific knowledge produced by humans? Influential opinion-makers claim that the human role in science will be taken over by machines. Chris Anderson’s (2008) provocative essay, The End of Theory: The Data Deluge Makes the Scientific Method Obsolete, will be taken as an exemplary expression of this opinion. The claim that machines will replace human scientists can be investigated within several perspectives (e.g., ethical, ethical-epistemological, practical and technical). This chapter focuses on epistemological aspects concerning ideas and beliefs about scientific knowledge. The approach is to point out epistemological views supporting the idea that machines can replace scientists, and to propose a plausible alternative that explains the role of scientists and human-made science, especially in view of the multitude of epistemic tasks in practical uses of knowledge. Whereas philosophical studies into machine learning often focus on reliability and trustworthiness, the focus of this chapter is on the usefulness of knowledge for epistemic tasks. This requires distinguishing between epistemic tasks for which machine learning is useful, versus those that require human scientists. In analyzing Anderson’s claim, a kind of double stroke is made. First, it will be made plausible that the fundamental presuppositions of empiricist epistemologies give reason to believe that machines will ultimately make scientists superfluous. Next, it is argued that empiricist epistemologies are deficient, because neglect the multitude of epistemic tasks for which humans need knowledge that is comprehensible for them. The character of machine learning technology is such that it does not provide such knowledge.
It will be concluded that machine learning is useful for specific types of epistemic tasks such as prediction, classification, and pattern-recognition, but for many other types of epistemic tasks—such as asking relevant questions, problem-analysis, interpreting problems as of a specific kind, designing interventions, and ‘seeing’ analogies that help to interpret a problem differently—the production and use of comprehensible scientific knowledge remains crucial.
Original languageEnglish
Title of host publicationA Critical Reflection on Automated Science
Subtitle of host publication Will Science Remain Human?
EditorsMarta Bertolaso, Fabio Sterpetti
PublisherSpringer International Publishing AG
Pages43-65
Volume1
ISBN (Electronic)978-3-030-25001-0
ISBN (Print)978-3-030-25000-3
DOIs
Publication statusPublished - 5 Mar 2020

Publication series

NameHuman Perspectives in Health Sciences and Technology
PublisherSpringer
Volume1
ISSN (Print)2661-8915
ISSN (Electronic)2661-8923

Keywords

  • Philosophy of science in practice
  • Empiricism
  • Logical positivism
  • Epistemic tool
  • Epistemic task
  • Machine-learning
  • Data-model
  • Engineering Sciences

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  • Cite this

    Boon, M. (2020). How Scientists Are Brought Back into Science—The Error of Empiricism. In M. Bertolaso, & F. Sterpetti (Eds.), A Critical Reflection on Automated Science: Will Science Remain Human? (Vol. 1, pp. 43-65). (Human Perspectives in Health Sciences and Technology; Vol. 1). Springer International Publishing AG. https://doi.org/10.1007/978-3-030-25001-0_4