When Should AI Read the Room? Public Perceptions of Social Intelligence in AI Agents

2026-05-28

AI Summary

This study addresses the limited understanding of public acceptance, evaluative criteria, and ethical concerns regarding AI agents endowed with social intelligence. Employing a mixed-methods approach combining surveys and in-depth interviews, it investigates how U.S. adults assess the social intelligence of multimodal AI agents—such as chatbots and embodied robots—based on observable behaviors rather than attributed intentions, and examines their willingness to accept or reject such agents across diverse contexts. The findings reveal that although most participants have interacted with AI they perceive as socially intelligent, they exhibit greater support for others’ use than for personal adoption, highlighting a pronounced “support–adoption gap.” This work provides the first systematic account of public perception mechanisms concerning AI social intelligence, offering critical empirical insights for informed deployment decisions and governance frameworks.

Abstract

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.

Problem

• social intelligence
• AI agents
• public perception
• acceptability
• human-AI interaction

Innovation

• social intelligence
• public perception
• AI acceptance
• mixed-methods survey
• support-adoption gap