Motesharei, Amir (2026) Exploration Before Representation: What Do Machine Learning Models Do in Science? In: UNSPECIFIED.

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Abstract

Philosophy of machine learning frequently frames what machine learning models do in science in target-oriented terms. This paper argues that this framing misses a significant modeling function: across many stages of scientific practice, machine learning models are used primarily for exploration, with target-oriented, representational assessment deferred. I develop a framework for exploratory machine learning models, characterizing them by their functional role in carving and exploring spaces of theoretical/experimental possibilities under target indeterminacy. I then propose robustness, calibration, and downstream investigative yield as primary evaluation criteria. I further argue that such workflows can warrant theoretically constrained modal claims and support how-possible inferences.

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Item Type:	Conference or Workshop Item (UNSPECIFIED)
Creators:	Creators Email ORCID Motesharei, Amir 0000-0002-8475-1615
Subjects:	Specific Sciences > Artificial Intelligence Specific Sciences > Artificial Intelligence > Machine Learning
Depositing User:	Mr. Amir Motesharei
Date Deposited:	31 May 2026 12:44
Last Modified:	31 May 2026 12:44
Item ID:	29818
Subjects:	Specific Sciences > Artificial Intelligence Specific Sciences > Artificial Intelligence > Machine Learning
Date:	19 November 2026
URI:	https://philsci-archive.pitt.edu/id/eprint/29818

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