Norelli, Maria Federica and Votsis, Ioannis and Williamson, Jon (2024) The Interplay of Data, Models, and Theories in Machine Learning. In: UNSPECIFIED.

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Abstract

This paper discusses the role of data within scientific reasoning and as evidence for theoretical claims, arguing for the idea that data can yield theoretically grounded models and be inferred, predicted, or explained from/by such models. Contrary to Bogen and Woodward's skepticism regarding the feasibility and epistemic relevance of data-to-theory and theory-to-data inferences, we draw upon scientific artificial intelligence literature to advocate that: a) many models are routinely inferred and predicted from the data and routinely used to infer and predict data: b) such models can, at least in some contexts, play the role of theoretical device.

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Item Type:	Conference or Workshop Item (UNSPECIFIED)
Creators:	Creators Email ORCID Norelli, Maria Federica mn2122@students.nulondon.ac.uk Votsis, Ioannis Williamson, Jon j.williamson@kent.ac.uk 0000-0003-0514-4209
Keywords:	data, phenomena, scientific theories, models
Subjects:	General Issues > Data Specific Sciences > Computer Science General Issues > Philosophers of Science
Depositing User:	Ms Maria Federica Norelli
Date Deposited:	04 Sep 2024 12:47
Last Modified:	04 Sep 2024 12:47
Item ID:	23870
Subjects:	General Issues > Data Specific Sciences > Computer Science General Issues > Philosophers of Science
Date:	2024
URI:	https://philsci-archive.pitt.edu/id/eprint/23870

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