Abstract Virtual agents offer a powerful and underutilized methodology for studying social interaction, particularly how bias and stereotypes operate. Drawing on theoretical frameworks such as the Media Equation and supported by empirical studies, we demonstrate how virtual agents can serve as controlled proxies for human social actors to study social bias. Through two empirical studies in the negotiation domain, we show how gender cues can be studied in negotiation contexts. Study 1 examines the effects of gendered job descriptions on negotiation behavior — finding that gendered language and gender interact to affect participants’ minimally acceptable salary. Study 2 explores how agent embodiment and emotional tone influence user perceptions across gender lines — finding that agent embodiment and one’s gender interact to affect participants’ subjective feelings about the dispute. Together, these findings imply the need for greater use of virtual agents in research, as they help inform the development of more inclusive and equitable AI systems.
Hale et al. (Fri,) studied this question.