Year: 2025
Type: article
Abstract: Fairness is a fundamental principle in human social interactions that influences subsequent behavioural decisions. As artificial intelligence (AI) becomes more prevalent, human-computer interactions have emerged as a new mode of social interaction. This study investigates the differences in fairness perceptions and their impact on trust decisions in human-human and human-AI contexts using a mixed experimental design of 2 (proposer identity: AI/human) × 2 (offer: fair/unfair) × 2 (trustee identity: AI/human). A total of 128 university students participated in the experimental study employing both the Ultimatum Game and the Trust Game paradigms. The results showed that participants who received fair offers had higher trust investment rates and amounts than those who received unfair offers. When offers were unfair, the AI proposer group elicited greater investment willingness, leading to higher trust investment rates than the human proposer group. Conversely, under fair conditions, participants displayed greater risk aversion towards human trustees, investing at lower rates and amounts than with AI trustees. The findings suggest that fairness perceptions in human-computer interactions have a stronger impact on trust decisions than those in human-human interactions.
DOI: 10.1002/ijop.70111
Authors: Rui Chen, Yating Jin, Lincang Yu, Tobias Tempel, Peng Li, Shi Zhang, Anqi Li, Weijie He
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,570
OpenAlex ID: W4414333052