Ratti, Emanuele and Ladyman, James and Termine, Alberto (2026) The Modelling Structure of AI-Based Science. [Preprint]

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Abstract

This book provides a unified account of models and model-building practice in AI-based science, particularly machine learning (ML). It analyzes the relationship between ML model-building practices and scientific domain knowledge, develops an account of ML models as technical artifacts structured around five levels of abstraction that captures their representational capabilities, and shows how this framework can be used to reformulate contemporary debates in philosophy of science and AI in more fruitful ways.

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Item Type:	Preprint
Creators:	Creators Email ORCID Ratti, Emanuele mnl.ratti@gmail.com 0000-0003-1409-8240 Ladyman, James james.ladyman@bristol.ac.uk Termine, Alberto alberto.termine@unimi.it 0000-0001-5993-0948
Additional Information:	Under contract for CUP; first draft, comments are welcome
Keywords:	machine learning; AI-based science; scientific models
Subjects:	Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Models and Idealization General Issues > Structure of Theories General Issues > Technology
Depositing User:	Dr Emanuele Ratti
Date Deposited:	02 Jun 2026 18:38
Last Modified:	02 Jun 2026 18:38
Item ID:	29886
Subjects:	Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Models and Idealization General Issues > Structure of Theories General Issues > Technology
Date:	2026
URI:	https://philsci-archive.pitt.edu/id/eprint/29886

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