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360Instruments, agents, and artificial intelligence: novel epistemic categories of reliabilitySynthese 200 (6): 1-20. 2022.Deep learning (DL) has become increasingly central to science, primarily due to its capacity to quickly, efficiently, and accurately predict and classify phenomena of scientific interest. This paper seeks to understand the principles that underwrite scientists’ epistemic entitlement to rely on DL in the first place and argues that these principles are philosophically novel. The question of this paper is not whether scientists can be justified in trusting in the reliability of DL. While today’s a…Read more
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301Deep Learning Opacity in Scientific DiscoveryPhilosophy of Science 90 (5). 2023.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 scienti…Read more
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Harvard UniversityPost-doctoral Fellow
University of Chicago
PhD, 2023
APA Central Division
Chicago, Illinois, United States of America
Areas of Specialization
Philosophy of Computing and Information |
General Philosophy of Science |
Epistemology |
Areas of Interest
Science, Logic, and Mathematics |
Epistemology |
Technology Ethics |
20th Century Philosophy |