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

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.