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Generative AI entails a credit–blame asymmetry

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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We wish to thank an anonymous reviewer for very helpful and timely suggestions for improvements to an earlier version of this article.

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B.D.E. and SPM conceived of the paper. B.D.E., S.P.M., J.D., S.N. & N.M. produced first drafts. All authors provided paragraphs of text and edited drafts.

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Correspondence to Sebastian Porsdam Mann or Brian D. Earp.

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The authors declare no competing interests.

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Nature Machine Intelligence thanks Carolyn Ashurst for their contribution to the peer review of this work.

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Porsdam Mann, S., Earp, B.D., Nyholm, S. et al. Generative AI entails a credit–blame asymmetry. Nat Mach Intell 5, 472–475 (2023).

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