Zakharova, Daria (2024) The Epistemology of AI-driven Science: The Case of AlphaFold. [Preprint]
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Abstract
The success of AlphaFold, an AI system that predicts protein structures, poses a challenge for traditional understanding of scientific knowledge. It operates opaquely, generating predictions without revealing the underlying principles behind its predictive success. Moreover, the predictions are largely not empirically tested but are taken at face value for further modelling purposes (e.g. in drug discovery) where experimentation takes place much further down the line. The paper presents a trilemma regarding the epistemology of AlphaFold, whereby we are forced to reject one of 3 claims: (1) AlphaFold produces scientific knowledge; (2) Predictions alone are not scientific knowledge unless derivable from established scientific principles; and (3) Scientific knowledge cannot be strongly opaque. The paper argues that AlphaFold's predictions function as scientific knowledge due to their trustworthiness and functional integration into scientific practice. The paper addresses the key challenge of strong opacity by drawing on Alexander Bird's functionalist account of scientific knowledge as irreducibly social, and advances the position against individual knowledge being necessary for the production of scientific knowledge. It argues that the implicit principles used by AlphaFold satisfy the conditions for scientific knowledge, despite their opacity. Scientific knowledge can be strongly opaque to humans, as long as it is properly functionally integrated into the collective scientific enterprise.
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