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

Law

University of Oxford
University of Oxford
Ludwig-Maximilians-University Munich
University of Oxford Medical Sciences Division
Monash University
University of Lucerne
South Bank University
Birkbeck University UK
Max-Planck-Institute of Biochemistry
Applied AI Corporation
Marshall Space Flight Control Center
University of California Berkeley
University of Otago
National University of Singapore

Research output: Contribution to a Journal (Peer & Non Peer) › Comment/debate

68 Citations (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 creditblame asymmetry arises for assigning responsibility for these outputs and discuss urgent ethical and policy implications focused on large-scale language models.

Original language	English
Pages (from-to)	472-475
Number of pages	4
Journal	Nature Machine Intelligence
Volume	5
Issue number	5
DOIs	https://doi.org/10.1038/s42256-023-00653-1
Publication status	Published - May 2023

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10.1038/s42256-023-00653-1

Cite this

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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 creditblame 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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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

PY - 2023/5

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N2 - Generative AI programs can produce high-quality written and visual content that may be used for good or ill. We argue that a creditblame asymmetry arises for assigning responsibility for these outputs and discuss urgent ethical and policy implications focused on large-scale language models.

AB - Generative AI programs can produce high-quality written and visual content that may be used for good or ill. We argue that a creditblame 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

SN - 2522-5839

VL - 5

SP - 472

EP - 475

JO - Nature Machine Intelligence

JF - Nature Machine Intelligence

IS - 5

ER -

Generative AI entails a credit–blame asymmetry

Abstract

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