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This paper investigates public perceptions of social intelligence in AI agents (Social-AI) through a mixed-methods survey of 200 US adults. The study reveals that people judge Social-AI based on observed behaviors rather than inferred agency, and that a significant "support-adoption gap" exists where individuals are more accepting of Social-AI for others than for themselves. The findings highlight key concerns about Social-AI deployment, informing AI governance and risk mitigation strategies.
People judge AI's social skills by its actions, not its supposed thoughts, but they're still wary of letting it into their own lives.
AI researchers have been advancing socially intelligent AI agents (Social-AI) across embodiments, from chatbots to physical robots. As Social-AI is increasingly deployed in everyday settings, decisions about the roles these agents should play will depend on how laypeople perceive them. However, public perceptions of social intelligence in AI agents and the acceptability of these agents remain largely understudied. We present a mixed-methods survey of adults in the United States (N=200) that examines social intelligence as a perceived construct in AI agents. Our survey investigates the extent to which participants believe current AI agents have social intelligence, abilities of agents that participants associate with social intelligence, contextual factors influencing participant acceptance of Social-AI agents, and concerns participants hold about these technologies. Participants widely reported having already encountered AI agents they perceived as socially intelligent and grounded their judgments in observable behaviors, more than beliefs about AI agency or intent. We identified a support-adoption gap in acceptability judgments: participants supported the existence of Social-AI agents for others far more than for their own personal use. Our analysis uncovers layperson concerns about Social-AI, informing AI governance regarding appropriate deployment contexts, agent roles, and risks to end users.
CMU ML