When Should AI Read the Room? Public Perceptions of Social Intelligence in AI Agents
- URL: http://arxiv.org/abs/2605.29938v1
- Date: Thu, 28 May 2026 13:49:47 GMT
- Title: When Should AI Read the Room? Public Perceptions of Social Intelligence in AI Agents
- Abstract summary: Social-AI agents are increasingly deployed in everyday settings.<n>Public perceptions of social intelligence in AI agents and the acceptability of these agents remain largely understudied.<n>We present a mixed-methods survey of adults in the United States.
- Score: 61.06747336710885
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- When Nobody Around Is Real: Exploring Public Opinions and User Experiences On the Multi-Agent AI Social Platform [5.133676030875285]
A new genre of multi-agent social platforms has emerged, powered by large language models.<n>Social.AI deploys numerous AI agents that emulate human behavior, creating unprecedented bot-centric social networks.<n>While some user expectations were met, the AI-dominant social environment introduces distinct problems.
arXiv Detail & Related papers (2026-01-26T08:58:51Z) - Conformity and Social Impact on AI Agents [42.04722694386303]
This study examines conformity, the tendency to align with group opinions under social pressure, in large multimodal language models functioning as AI agents.<n>Our experiments reveal that AI agents exhibit a systematic conformity bias, aligned with Social Impact Theory, showing sensitivity to group size, unanimity, task difficulty, and source characteristics.<n>These findings reveal fundamental security vulnerabilities in AI agent decision-making that could enable malicious manipulation, misinformation campaigns, and bias propagation in multi-agent systems.
arXiv Detail & Related papers (2026-01-08T21:16:28Z) - Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions [67.60397632819202]
Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal.
We identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI.
arXiv Detail & Related papers (2024-04-17T02:57:42Z) - AI for social science and social science of AI: A Survey [47.5235291525383]
Recent advancements in artificial intelligence have sparked a rethinking of artificial general intelligence possibilities.
The increasing human-like capabilities of AI are also attracting attention in social science research.
arXiv Detail & Related papers (2024-01-22T10:57:09Z) - SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents [107.4138224020773]
We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and humans.
In our environment, agents role-play and interact under a wide variety of scenarios; they coordinate, collaborate, exchange, and compete with each other to achieve complex social goals.
We find that GPT-4 achieves a significantly lower goal completion rate than humans and struggles to exhibit social commonsense reasoning and strategic communication skills.
arXiv Detail & Related papers (2023-10-18T02:27:01Z) - The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI)
We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents.
We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
arXiv Detail & Related papers (2023-09-14T17:12:03Z) - Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time.
We discuss how biased models can lead to more negative real-world outcomes for certain groups.
If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
arXiv Detail & Related papers (2023-04-16T11:22:59Z) - The Role of Social Movements, Coalitions, and Workers in Resisting
Harmful Artificial Intelligence and Contributing to the Development of
Responsible AI [0.0]
Coalitions in all sectors are acting worldwide to resist hamful applications of AI.
There are biased, wrongful, and disturbing assumptions embedded in AI algorithms.
Perhaps one of the greatest contributions of AI will be to make us understand how important human wisdom truly is in life on earth.
arXiv Detail & Related papers (2021-07-11T18:51:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.