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Deepfakes, Fake Barns, and Knowledge from Videos
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 clo...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer Netherlands
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9869812/ https://www.ncbi.nlm.nih.gov/pubmed/36714268 http://dx.doi.org/10.1007/s11229-022-04033-x |
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author | Matthews, Taylor |
author_facet | Matthews, Taylor |
author_sort | Matthews, Taylor |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9869812 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-98698122023-01-25 Deepfakes, Fake Barns, and Knowledge from Videos Matthews, Taylor Synthese Original Research 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. Springer Netherlands 2023-01-23 2023 /pmc/articles/PMC9869812/ /pubmed/36714268 http://dx.doi.org/10.1007/s11229-022-04033-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Research Matthews, Taylor Deepfakes, Fake Barns, and Knowledge from Videos |
title | Deepfakes, Fake Barns, and Knowledge from Videos |
title_full | Deepfakes, Fake Barns, and Knowledge from Videos |
title_fullStr | Deepfakes, Fake Barns, and Knowledge from Videos |
title_full_unstemmed | Deepfakes, Fake Barns, and Knowledge from Videos |
title_short | Deepfakes, Fake Barns, and Knowledge from Videos |
title_sort | deepfakes, fake barns, and knowledge from videos |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9869812/ https://www.ncbi.nlm.nih.gov/pubmed/36714268 http://dx.doi.org/10.1007/s11229-022-04033-x |
work_keys_str_mv | AT matthewstaylor deepfakesfakebarnsandknowledgefromvideos |