The Effects of (Dis)similarities Between the Creator and the Assessor on Assessing Creativity: A Comparison of Humans and LLMs.

Current research predominantly involves human subjects to evaluate AI creativity. In this explorative study, we questioned the validity of this practice and examined how creator-assessor (dis)similarity-namely to what extent the creator and the assessor were alike-along two dimensions of culture (Western and English-speaking vs. Eastern and Chinese-speaking) and agency (human vs. AI) influences the assessment of creativity. We first asked four types of subjects to create stories, including Eastern participants (university students from China), Eastern AI (Kimi from China), Western participants (university students from The Netherlands), and Western AI (ChatGPT 3.5 from the US). Both Eastern participants and AI created stories in Chinese, which were then translated into English, while both Western participants and AI created stories in English, which were then translated into Chinese. A subset of these stories (2 creative and 2 uncreative per creator type, in total 16 stories) was then randomly selected as assessment materials. Adopting a within-subject design, we then asked new subjects from the same four types ( = 120, 30 per type) to assess these stories on creativity, originality, and appropriateness. The results confirmed that similarities in both dimensions of culture and agency influence the assessment of originality and appropriateness. As for the agency dimension, human assessors preferred human-created stories for originality, while AI assessors showed no preference. Conversely, AI assessors rated AI-generated stories higher in appropriateness, whereas human assessors showed no preference. Culturally, both Eastern and Western assessors favored Eastern-created stories in originality. And as for appropriateness, the assessors always preferred stories from the creators with the same cultural backgrounds. The present study is significant in attempting to ask an often-overlooked question and provides the first empirical evidence to underscore the need for more discussion on using humans to judge AI agents' creativity or the other way around.