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  Generative AI entails a credit-blame asymmetry

Porsdam Mann, S., Earp, B. D., Nyholm, S., Danaher, J., Moller, N., Bowman-Smart, H., et al. (2023). Generative AI entails a credit-blame asymmetry. Nature Machine Intelligence, 5, 472-475. doi:10.1038/s42256-023-00653-1.

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 Creators:
Porsdam Mann, Sebastian1, Author
Earp, Brian D.1, Author
Nyholm, Sven1, Author
Danaher, John1, Author
Moller, Nikolaj1, Author
Bowman-Smart, Hilary1, Author
Hatherley, Joshua1, Author
Koplin, Julian1, Author
Plozza, Monika1, Author
Rodger, Daniel1, Author
Treit, Peter V.2, Author           
Renard, Gregory1, Author
McMillan, John1, Author
Savulescu, Julian1, Author
Affiliations:
1external, ou_persistent22              
2Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society, ou_1565159              

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Free keywords: Computer Science;
 Abstract: Generative AI programs can produce high-quality written and visual content that may be used for good or ill. We argue that a credit-blame asymmetry arises for assigning responsibility for these outputs and discuss urgent ethical and policy implications focused on large-scale language models.

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Language(s): eng - English
 Dates: 2023-05-042023-05
 Publication Status: Issued
 Pages: 4
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Degree: -

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Title: Nature Machine Intelligence
Source Genre: Journal
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Publ. Info: London : Springer Nature Publishing
Pages: - Volume / Issue: 5 Sequence Number: - Start / End Page: 472 - 475 Identifier: ISSN: 2522-5839
CoNE: https://pure.mpg.de/cone/journals/resource/2522-5839