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Generative AI entails a credit–blame asymmetry

Nature Machine Intelligence volume 5, pages 472–475 (2023)Cite this article

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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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Acknowledgements

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

Author information

Authors and Affiliations

  1. Bonavero Institute of Human Rights, Faculty of Law, University of Oxford, Oxford, UK

    Sebastian Porsdam Mann

  2. Uehiro Centre of Practical Ethics, University of Oxford, Oxford, UK

    Brian D. Earp & Nikolaj Møller

  3. Faculty of Philosophy, Philosophy of Science, and Religious Studies, LMU Munich, Munich, Germany

    Sven Nyholm

  4. School of Law, University of Galway, Galway, Ireland

    John Danaher

  5. Ethox Centre, University of Oxford, Oxford, UK

    Hilary Bowman-Smart

  6. Monash Bioethics Centre, Monash University, Melbourne, Victoria, Australia

    Hilary Bowman-Smart & Julian Koplin

  7. School of Philosophical, Historical, and International Studies, Monash University, Melbourne, Victoria, Australia

    Joshua Hatherley

  8. Faculty of Law, University of Lucerne, Lucerne, Switzerland

    Monika Plozza

  9. Institute of Health and Social Care, School of Allied and Community Health, London South Bank University, London, UK

    Daniel Rodger

  10. Department of Psychological Sciences, Birkbeck, University of London, London, UK

    Daniel Rodger

  11. Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Munich, Germany

    Peter V. Treit

  12. Applied AI Corporation, San Francisco, CA, USA

    Gregory Renard

  13. Frontier Development Lab, SETI, NASA, Mountain View, CA, USA

    Gregory Renard

  14. University of California, Berkeley, CA, USA

    Gregory Renard

  15. Department of Bioethics, University of Otago, Dunedin, Otago, New Zealand

    John McMillan

  16. Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Julian Savulescu

Authors
  1. Sebastian Porsdam Mann
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  2. Brian D. Earp
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  4. John Danaher
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  5. Nikolaj Møller
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  6. Hilary Bowman-Smart
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  7. Joshua Hatherley
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  8. Julian Koplin
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  9. Monika Plozza
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  10. Daniel Rodger
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  11. Peter V. Treit
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  12. Gregory Renard
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  13. John McMillan
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  14. Julian Savulescu
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Contributions

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.

Corresponding authors

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). https://doi.org/10.1038/s42256-023-00653-1

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