How scientists are brought back into science – The error of empiricism

Activity: Talk or presentationOral presentation


ABSTRACT: This paper aims at a contribution 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 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 to distinguish 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 it neglects the multitude of epistemic tasks of and by humans, 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. References:Abu-Mostafa, Y.S., Magdon-Ismail, M., and Lin, H-T. (2012). Learning from data. Alpaydin, E. (2010). Introduction to machine learning. The MIT Press: Cambridge.Anderson, C. (2008). The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired Magazine June 23, 2008. Retrieved from: Bogen, J., & Woodward, J. (1988). Saving the Phenomena. The Philosophical Review, 97(3), 303-352. doi:10.2307/2185445 Boon, M., & Knuuttila, T. (2009). Models as Epistemic Tools in Engineering Sciences: a Pragmatic Approach. In A. Meijers (Ed.), Philosophy of technology and engineering sciences. Handbook of the philosophy of science (Vol. 9, pp. 687-720): Elsevier/North-HollandHumphreys, P. (2009). The philosophical novelty of computer simulation methods. Synthese, 169(3), 615-626. doi:10.1007/s11229-008-9435-2Nersessian, N. J. (2009). Creating Scientific Concepts. Cambridge, MA: MIT Press.Suppe, F. (1974). The Structure of Scientific Theories (1979 (second printing ed.). Urbana: University of Illinois Press.Suppe, F. (1989). The Semantic Conception of Theories and Scientific Realism. Urbana and Chicago: University of Illinois Press.Suppes, P. (1960). A Comparison of the Meaning and Uses of Models in Mathematics and the Empirical Sciences. Synthese, 12, 287-301. Van Fraassen, B. C. (1977). The pragmatics of explanation. American Philosophical Quarterly, 14, 143-150. Van Fraassen, B. C. (1980). The Scientific Image. Oxford: Clarendon Press.
Period5 Aug 201910 Aug 2019
Event title16th International Congress on Logic, Methodology and Philosophy of Science and Technology 2019
Event typeConference
Conference number16
LocationPrague, Czech RepublicShow on map
Degree of RecognitionInternational


  • Philosophy of Science in Practice
  • Empiricism
  • Logical Positivism
  • Phenomena
  • Epistemic tool
  • Epistemic task
  • Machine Learning
  • Data model
  • Engineering sciences
  • Biomedical Sciences