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Deep Learning Opacity in Scientific Discovery

Duede, Eamon (2022) Deep Learning Opacity in Scientific Discovery. In: UNSPECIFIED.

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Philosophers have recently focused on critical, epistemological challenges that arise from the opacity of deep neural networks. One might conclude from this literature that doing good science with opaque models is exceptionally challenging, if not impossible. Yet, this is hard to square with the recent boom in optimism for AI in science alongside a flood of recent scientific breakthroughs driven by AI methods. In this paper, I argue that the disconnect between philosophical pessimism and scientific optimism is driven by a failure to examine how AI is actually used in science. I show that, in order to understand the epistemic justification for AI-powered breakthroughs, philosophers must examine the role played by deep learning as part of a wider process of discovery. The philosophical distinction between the 'context of discovery' and the 'context of justification' is helpful in this regard. I demonstrate the importance of attending to this distinction with two cases drawn from the scientific literature, and show that epistemic opacity need not diminish AI's capacity to lead scientists to significant and justifiable breakthroughs.

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Item Type: Conference or Workshop Item (UNSPECIFIED)
Duede, Eamoneduede@uchicago.edu0000-0002-3592-0478
Keywords: Artificial Intelligence Epistemology Justification Discovery
Subjects: Specific Sciences > Computer Science
Specific Sciences > Artificial Intelligence
Depositing User: Eamon Duede
Date Deposited: 24 Aug 2022 12:51
Last Modified: 24 Aug 2022 12:51
Item ID: 21085
Subjects: Specific Sciences > Computer Science
Specific Sciences > Artificial Intelligence
Date: 1 June 2022

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