Apologizing artificial intelligence: designing and evaluating effective AI apologies after errors
Crossref DOI link: https://doi.org/10.1007/s00146-026-03067-w
Published Online: 2026-05-12
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Turel, Ofir
Cui, Tingru
Funding for this research was provided by:
University of Melbourne
Text and Data Mining valid from 2026-05-12
Version of Record valid from 2026-05-12
Article History
Received: 23 April 2025
Accepted: 16 April 2026
First Online: 12 May 2026
Declarations
:
: The authors declare no competing interests.