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Mechanistic Models and the Explanatory Limits of Machine Learning

Ratti, Emanuele and López-Rubio, Ezequiel (2018) Mechanistic Models and the Explanatory Limits of Machine Learning. In: UNSPECIFIED.

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Abstract

We argue that mechanistic models elaborated by machine learning cannot be explanatory by discussing the relation between mechanistic models, explanation and the notion of intelligibility of models. We show that the ability of biologists to understand the model that they work with (i.e. intelligibility) severely constrains their capacity of turning the model into an explanatory model. The more a mechanistic model is complex (i.e. it includes an increasing number of components), the less explanatory it will be. Since machine learning increases its performances when more components are added, then it generates models which are not intelligible, and hence not explanatory.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Creators:
CreatorsEmailORCID
Ratti, Emanuelemnl.ratti@gmail.com
López-Rubio, Ezequiel
Keywords: mechanistic model; mechanistic explanation; machine learning; intelligibility
Subjects: Specific Sciences > Biology > Molecular Biology/Genetics
Specific Sciences > Artificial Intelligence
General Issues > Explanation
Depositing User: Dr Emanuele Ratti
Date Deposited: 13 Mar 2018 00:26
Last Modified: 13 Mar 2018 00:26
Item ID: 14452
Subjects: Specific Sciences > Biology > Molecular Biology/Genetics
Specific Sciences > Artificial Intelligence
General Issues > Explanation
Date: 2018
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/14452

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