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 for this work

Research output: Contribution to journalComment/debateResearchpeer-review

33 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 credit–blame asymmetry arises for assigning responsibility for these outputs and discuss urgent ethical and policy implications focused on large-scale language models.
Original languageEnglish
JournalNature Machine Intelligence
Volume5
Issue number5
Pages (from-to)472-475
Number of pages4
DOIs
Publication statusPublished - 2023

Cite this