Generative AI entails a credit–blame asymmetry

Sebastian Porsdam Mann; Brian D. Earp; Sven Nyholm; John Danaher; Nikolaj Møller; Hilary Bowman-Smart; Joshua Hatherley; Julian Koplin; Monika Plozza; Daniel Rodger; Peter V. Treit; Gregory Renard; John McMillan; Julian Savulescu

doi:10.1038/s42256-023-00653-1

Generative AI entails a credit–blame asymmetry

Sebastian Porsdam Mann^*, Brian D. Earp, Sven Nyholm, John Danaher, Nikolaj Møller, Hilary Bowman-Smart, Joshua Hatherley, Julian Koplin, Monika Plozza, Daniel Rodger, Peter V. Treit, Gregory Renard, John McMillan, Julian Savulescu

^*Corresponding author af dette arbejde

Center for Interdisciplinære Retlige Studier

Publikation: Bidrag til tidsskrift › Kommentar/debat › Forskning › peer review

66 Citationer (Scopus)

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.

Originalsprog	Engelsk
Tidsskrift	Nature Machine Intelligence
Vol/bind	5
Udgave nummer	5
Sider (fra-til)	472-475
Antal sider	4
DOI	https://doi.org/10.1038/s42256-023-00653-1
Status	Udgivet - 2023

Bibliografisk note

Funding Information:
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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Citationsformater

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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.",

author = "{Porsdam Mann}, Sebastian and Earp, {Brian D.} and Sven Nyholm and John Danaher and Nikolaj M{\o}ller and Hilary Bowman-Smart and Joshua Hatherley and Julian Koplin and Monika Plozza and Daniel Rodger and Treit, {Peter V.} and Gregory Renard and John McMillan and Julian Savulescu",

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AU - Porsdam Mann, Sebastian

AU - Earp, Brian D.

AU - Nyholm, Sven

AU - Danaher, John

AU - Møller, Nikolaj

AU - Bowman-Smart, Hilary

AU - Hatherley, Joshua

AU - Koplin, Julian

AU - Plozza, Monika

AU - Rodger, Daniel

AU - Treit, Peter V.

AU - Renard, Gregory

AU - McMillan, John

AU - Savulescu, Julian

N1 - Funding Information: We wish to thank an anonymous reviewer for very helpful and timely suggestions for improvements to an earlier version of this article.

PY - 2023

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AB - 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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DO - 10.1038/s42256-023-00653-1

M3 - Comment/debate

AN - SCOPUS:85158030467

VL - 5

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JO - Nature Machine Intelligence

JF - Nature Machine Intelligence

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