When Behavior Is Not Enough: Identity Biases in Human-AI Creative Collaboration
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초록

As AI systems increasingly engage in creative collaboration, they are often designed with the assumption that appropriate behavior will foster perceptions of equal partnership. However, it remains unclear whether partner identity continues to shape perceptions in open-ended creative tasks when behavior is held constant. To examine how perceived partner identity (AI vs. human) and partner behavior (cooperative vs. aggressive) shape perceptions and behaviors, we used collaborative drawing, a turn-based non-verbal method from art therapy. In a web-based deception study (N = 30), participants collaborated with a human confederate while being told their partner was AI or human. While cooperative behavior improved overall perceptions regardless of identity, creativity ratings consistently favored human partners despite identical behavior. Participants also adopted different collaboration strategies depending on perceived identity. These findings indicate that cooperative behavior alone is insufficient to overcome identity-based biases in creative collaboration, highlighting the structural limit of behavior-centered AI design in creative collaboration.

키워드

artificial intelligencecollaborative drawingHuman-AI interaction
제목
When Behavior Is Not Enough: Identity Biases in Human-AI Creative Collaboration
저자
Won, HyunseonKwak, HaewoonAn, JisunHan, Jinyoung
DOI
10.1145/3772363.3799001
발행일
2026
유형
Conference Paper
저널명
Conference on Human Factors in Computing Systems - Proceedings