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More and more sciences are turning to machine learning (ML) technologies to solve long-standing problems or make new discoveries—ranging from medical science to fundamental physics. The ever-growing fingerprint ML modeling has on the production of scientific knowledge and understanding comes with new opportunities and also pressing challenges. In this talk, I discuss how philosophy of science and epistemology can help us understand the potential and limits of ML used for science. Specifically, I will argue that ML models in science function in a similar way that highly idealized toy models do. Thinking of ML models as toy models can help to shed light on the scope of ML’s potential for scientific understanding.
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Emily Sullivan is an assistant professor at Utrecht University. Her work is in epistemology and philosophy of science focusing on questions of scientific understanding, how technology mediates knowledge, and norms of testimony. She is an Associate Editor at the British Journal for the Philosophy of Science and is currently the PI on an Dutch Research Council personal research grant (Veni grant) on the explainablity of ML models through April 2024.