How Agents Represent Humans: Human-Directed Stereotypes in an Open Agent Social Network

2026-08-22

AI Summary

研究通过分析Moltbook平台上AI代理对人类的描述，采用四维度评价框架，探讨了代理如何构建人类形象及其中存在的偏见问题。

Abstract

LLM-based agents are increasingly deployed in persistent social environments, where generated claims can be posted, replied to, remembered, and reused. We study human-directed stereotypes on Moltbook, an open agent-native social platform, asking how agents construct humans as a social category. For this human-target analysis, we introduce an annotation framework with four evaluative dimensions---morality, friendliness, competence, and autonomy---and a second-stage subtype scheme for descriptive \textit{other} attributions. We find that competence dominates human-directed evaluations, while many \textit{other} attributions describe humans as epistemic, cultural, or embodied subjects. We further examine how these human representations appear in human--agent narrative contexts and platform-level circulation. As an auxiliary comparison, we analyze agent-internal community feedback through behavioral host affinity. Rather than reproducing the stable insider--outsider rejection often observed in human online communities, Moltbook feedback patterns are better explained by exposure, author visibility, and content selection. These findings suggest that bias in agent societies should be studied not only as isolated model output, but also as a discourse process.

Problem

• LLM-based agents
• social category
• stereotypes
• Moltbook
• human-directed

Innovation

• human-directed stereotypes
• social category construction
• agent-native social platform
• behavioral host affinity
• discourse process