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Aspects of Theory-Ladenness in Data-Intensive Science

Published online by Cambridge University Press:  01 January 2022

Wolfgang Pietsch
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Abstract

Recent claims, mainly from computer scientists, concerning a largely automated and model-free data-intensive science have been criticized by several philosophers of science. The debate suffers from lack of detail regarding the actual methods used in data-intensive science and in which ways these presuppose theoretical assumptions. I examine two widely used algorithms, classificatory trees and nonparametric regression, and argue that they are theory laden in an external sense, regarding the framing of research questions, but not in an internal sense, concerning the causal structure of the examined phenomenon. With respect to the novelty of data-intensive science, I draw an analogy to exploratory experimentation.

Type
Confirmation Theory
Information
Philosophy of Science , Volume 82 , Issue 5 , December 2015 , pp. 905 - 916
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

†

I am grateful to Mathias Frisch, Sabina Leonelli, and Sylvester Tremmel for very helpful insights and discussions.

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