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Instruments, agents, and artificial intelligence: novel epistemic categories of reliability.

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...

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Published in:Synthese Vol. 200; no. 6; pp. 1 - 23
Main Author: Duede, Eamon
Format: Article
Published: Springer Nature Dec2022
Online Access:View this record in EBSCOhost
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      pub: Springer Nature
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        10.1007/s11229-022-03975-6
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        atl: Instruments, agents, and artificial intelligence: novel epistemic categories of reliability.
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        au: Duede, Eamon
        affil:
          Department of Philosophy, University of Chicago, Chicago, USA
          Committee on Conceptual & Historical Studies of Science, University of Chicago, Chicago, USA
          Pritzker School of Molecular Engineering, University of Chicago, Chicago, USA
          Knowledge Lab, University of Chicago, Chicago, USA
      sug:
      keyword:
        Deep learning
        Models
        Reliability
        Scientific knowledge
        Trust and Justification
      ab: 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 artificial intelligence exhibits characteristics common to both scientific instruments and scientific experts, this paper argues that the familiar epistemic categories that justify belief in the reliability of instruments and experts are distinct, and that belief in the reliability of DL cannot be reduced to either. Understanding what can justify belief in AI reliability represents an occasion and opportunity for exciting, new philosophy of science.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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