Deepfakes, Fake Barns, and Knowledge from Videos

Bibliographic Data

ID10812707
AuthorsTaylor Matthews (0000-0002-3550-1898, University of Nottingham, corresponding author)
Year2023
Volume201
Issue2
Pages41-41
Publication date2023-01-23
Open AccessYes
TypeARTICLE
VenueSynthese (JOURNAL)
Journal identifiersISSN: 0039-7857 • E-ISSN: 1573-0964
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s11229-022-04033-x
PMID36714268
OpenAlexW4317745791
LanguageEN
Citations received10
References cited43

Recent develops in AI technology have led to increasingly sophisticated forms of video manipulation. One such form has been the advent of deepfakes. Deepfakes are AI-generated videos that typically depict people doing and saying things they never did. In this paper, I demonstrate that there is a close structural relationship between deepfakes and more traditional fake barn cases in epistemology. Specifically, I argue that deepfakes generate an analogous degree of epistemic risk to that which is found in traditional cases. Given that barn cases have posed a long-standing challenge for virtue-theoretic accounts of knowledge, I consider whether a similar challenge extends to deepfakes. In doing so, I consider how Duncan Pritchard’s recent anti-risk virtue epistemology meets the challenge. While Pritchard’s account avoids problems in traditional barn cases, I claim that it leads to local scepticism about knowledge from online videos in the case of deepfakes. I end by considering how two alternative virtue-theoretic approaches might vindicate our epistemic dependence on videos in an increasingly digital world

Barn · Epistemic virtue · Epistemology · Metaphysics · Philosophy of language · Philosophy of science · Skepticism · Sociology · Virtue · Artificial Intelligence · Computer Science · Epistemology, Ethics, and Metaphysics · History · Misinformation and Its Impacts · Philosophy · Psychology of Moral and Emotional Judgment

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