The Automated Laplacean Demon: How ML Challenges Our Views on Prediction and Explanation

Minds and Machines 32 (1):159-183 (2021)
  Copy   BIBTEX

Abstract

Certain characteristics make machine learning a powerful tool for processing large amounts of data, and also particularly unsuitable for explanatory purposes. There are worries that its increasing use in science may sideline the explanatory goals of research. We analyze the key characteristics of ML that might have implications for the future directions in scientific research: epistemic opacity and the ‘theory-agnostic’ modeling. These characteristics are further analyzed in a comparison of ML with the traditional statistical methods, in order to demonstrate what it is specifically that makes ML methodology substantially unsuitable for reaching explanations. The analysis is given broader philosophical context by connecting it with the views on the role of prediction and explanation in science, their relationship, and the value of explanation. We proceed to show, first, that ML disrupts the relationship between prediction and explanation commonly understood as a functional relationship. Then we show that the value of explanation is not exhausted in purely predictive functions, but rather has a ubiquitously recognized value for both science and everyday life. We then invoke two hypothetical scenarios with different degrees of automatization of science, which help test our intuitions on the role of explanation in science. The main question we address is whether ML will reorient or otherwise impact our standard explanatory practice. We conclude with a prognosis that ML would diversify science into purely predictively oriented research based on ML-like techniques and, on the other hand, remaining faithful to anthropocentric research focused on the search for explanation.

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 92,283

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

Holism and Emergence: Dynamical Complexity Defeats Laplace’s Demon.John Collier - 2011 - South African Journal of Philosophy 30 (2):229-243.
Laplaceanism defended.Peter Gildenhuys - 2016 - Biology and Philosophy 31 (3):395-408.
Reintroducing prediction to explanation.Heather E. Douglas - 2009 - Philosophy of Science 76 (4):444-463.
Chaos, prediction and laplacean determinism.M. A. Stone - 1989 - American Philosophical Quarterly 26 (2):123--31.
The New Evil Demon and the Devil in the Details.Mikkel Gerken - 2018 - In Veli Mitova (ed.), The Factive Turn in Epistemology. Cambridge University Press. pp. 102-122.
Explanation in Physics: Explanation.Michael Redhead - 1990 - Royal Institute of Philosophy Supplement 27:135-154.
How to entrain your evil demon.Jakob Hohwy - 2017 - Philosophy and Predictive Processing.
The Instrumental Value of Explanations.Tania Lombrozo - 2011 - Philosophy Compass 6 (8):539-551.
State of the Field: Why novel prediction matters.Heather Douglas & P. D. Magnus - 2013 - Studies in History and Philosophy of Science Part A 44 (4):580-589.

Analytics

Added to PP
2021-10-17

Downloads
87 (#196,094)

6 months
29 (#108,465)

Historical graph of downloads
How can I increase my downloads?

Author Profiles

Sanja Sreckovic
University of Belgrade
Andrea Berber
University of Belgrade
Nenad Filipovic
University of Belgrade