Generative AI entails a credit-blame asymmetry

Journal article

Porsdam Mann, S., Earp, B.D., Nyholm, S., Danaher, J., Møller, N., Bowman-Smart, H., Hatherley, J., Koplin, J., Plozza, M., Rodger, D., Treit, P.V., Renard, G., McMillan, J. and Savulescu, J. (2023). Generative AI entails a credit-blame asymmetry. Nature Machine Intelligence. 5, pp. 472-475. https://doi.org/10.1038/s42256-023-00653-1
AuthorsPorsdam Mann, S., Earp, B.D., Nyholm, S., Danaher, J., Møller, N., Bowman-Smart, H., Hatherley, J., Koplin, J., Plozza, M., Rodger, D., Treit, P.V., Renard, G., McMillan, J. and Savulescu, J.
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.

KeywordsChatGPT, Large-scale language models, Authorship, AI, Responsibility, Transparency, Rights, Interests, Achievement
Year2023
JournalNature Machine Intelligence
Journal citation5, pp. 472-475
PublisherNature Research
ISSN 2522-5839
Digital Object Identifier (DOI)https://doi.org/10.1038/s42256-023-00653-1
Web address (URL)https://doi.org/10.1038/s42256-023-00653-1
Publication dates
Online04 May 2023
Publication process dates
Accepted06 Apr 2023
Deposited24 Apr 2023
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Open
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