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What Kind of Novelties Can Machine Learning Possibly Generate? The Case of Genomics

Ratti, Emanuele (2020) What Kind of Novelties Can Machine Learning Possibly Generate? The Case of Genomics. [Preprint]

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Abstract

Machine learning (ML) has been praised as a tool that can advance science and knowledge in radical ways. However, it is not clear exactly how radical are the novelties that ML generates. In this article, I argue that this question can only be answered contextually, because outputs generated by ML have to be evaluated on the basis of the theory of the science to which ML is applied. In particular, I analyze the problem of novelty of ML outputs in the context of molecular biology. In order to do this, I first clarify the nature of the models generated by ML. Next, I distinguish three ways in which a model can be novel (from the weakest to the strongest). Third, I dissect the way ML algorithms work and generate models in molecular biology and genomics. On these bases, I argue that ML is either a tool to identify instances of knowledge already present and codified, or to generate models that are novel in a weak sense. The notable contribution of ML to scientific discovery in the context of biology is that it can aid humans in overcoming
potential bias by exploring more systematically the space of possible hypotheses implied by a theory.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Ratti, Emanuelemnl.ratti@gmail.com
Additional Information: Accepted for publication in Studies in the History and Philosophy of Science Part A
Keywords: machine learning; theory pursuing; theory choice; molecular biology; genomics
Subjects: Specific Sciences > Biology > Molecular Biology/Genetics
Specific Sciences > Computer Science
Specific Sciences > Artificial Intelligence
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Theory Change
Depositing User: Dr Emanuele Ratti
Date Deposited: 18 Mar 2020 04:35
Last Modified: 18 Mar 2020 04:35
Item ID: 17008
Subjects: Specific Sciences > Biology > Molecular Biology/Genetics
Specific Sciences > Computer Science
Specific Sciences > Artificial Intelligence
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Theory Change
Date: 2020
URI: http://philsci-archive.pitt.edu/id/eprint/17008

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