Credit and blame for AI–generated content: Effects of personalization in four countries
Brian D. Earp, Sebastian Porsdam Mann, Peng Liu, Ivar Hannikainen, Maryam Ali Khan, Yueying Chu, Julian Savulescu · 2024
Read Credit and blame for AI–generated content: Effects of personalization in four countries by Brian D. Earp, Sebastian Porsdam Mann, Peng Liu, Ivar Hannikainen, Maryam Ali Khan, Yueying Chu, Julian Savulescu (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11668494.txt.
Description
Brian Earp led a team asking who gets credit or blame for content made with AI, in a 2024 study. The survey ran in four countries with a large, representative sample. It compared a standard large language model, a version tuned on the user's own past work, and no AI help at all.
People gave more credit for good outputs to users of the tuned model than to users of the standard one. Blame for harmful outputs barely shifted with model type, except partly in one country. British participants also blamed users of any model more than those who worked unaided. The authors discuss what this means for ethics and policy.
Catalog record
| CREATOR | Brian D. Earp, Sebastian Porsdam Mann, Peng Liu, Ivar Hannikainen, Maryam Ali Khan, Yueying Chu, Julian Savulescu |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 25 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |