Ai companions show racial traits that affect how people see them
Stereotypically Yours: Portrayal and Perception of Race-Coded AI Companions
Human-Computer Interaction
Summary
AI companions can take on traits linked to different races, and this changes how users feel about and understand them. The authors found that AI personas coded as Asian men acted more submissive, while those coded as Black, Hispanic, or Indigenous men showed more aggression compared to White personas. People who used these AI companions had different opinions about how much race should show up—some wanted cultural connections without obvious stereotypes. The authors say that making AI companions truly respectful of race is hard because social ideas and stereotypes get in the way.
What this means in practice
- •For ai product designers: Use nuanced racial persona audits to avoid reinforcing stereotypes in AI companions and offer culturally relevant but respectful interactions.
- •For customer experience teams: Evaluate AI companion personalization beyond user satisfaction by identifying potential racial representation harms that affect user perception and brand trust.
Authors
Wang Claire, Jiayue Melissa Shi, Agam Goyal, Grace Sletten, Renwen Zhang, Eshwar Chandrasekharan, Koustuv Saha
Abstract
AI companions can purportedly adopt racial personas, raising questions about how they represent identity and how users interpret these portrayals. We combined an algorithmic audit of race-coded AI personas with interviews with 12 companion users who interacted with a probe. Our audit revealed systematic differences, such as Asian-coded male personas receiving higher submissiveness scores than White counterparts, and Black, Hispanic, and Indigenous male personas receiving higher aggression scores than their White counterparts in open-weight models. Interviews revealed that participants envisioned AI companions as offering cultural familiarity and outside perspectives, but differed in which portrayals they considered meaningful or stereotypical. Some rejected overt racial signaling while still expecting culturally distinctive responses. Triangulating these findings with theory, we highlight how social norms and cultural expectations complicate efforts to support meaningful racial representation without reproducing stereotypes. We discuss how companion personalization should be evaluated beyond user satisfaction to account for broader representational harms.