这篇论文把社会学搬进AI对齐，让智能体学会协调不同人的看法，而不是只追求主流答案。
arXiv预印本2608.03910提出，AI对齐不能只优化单一价值，需处理多元合法视角。论文指出现有方法缺乏对价值社会组织、争论和协调三个过程的清晰解释。作者借用社会学传统，给出三种设计方向：基于角色的表示、视角间结构化协调、上下文敏感评估。对智能体系统，需对齐角色激活、审议轨迹、聚合规则和反馈回路四个环节。
Socially Grounded Agentic AI: Coordinating Plural Perspectives through Social Theory
As AI systems are deployed across increasingly diverse social contexts, alignment can no longer be framed as the optimization of a single, unified set of values. Instead, systems must be able to recognize, represent, and respond to multiple legitimate perspectives. This has led to growing interest in pluralistic alignment, which seeks to move beyond one-size-fits-all models of appropriate behaviour. However, current approaches often lack a clear account of how values are socially organized, contested, and coordinated in practice. In this paper, we argue that social theory provides essential conceptual and design resources for addressing these challenges. Drawing on established traditions in sociology, we show how perspectives can be understood as structured by roles, shaped through interaction, and distributed across fields of power and expertise. We translate these insights into concrete implications for AI system design, including role-based representations, structured coordination among perspectives, and context-sensitive evaluation. For agentic systems, this requires aligning not only final outputs, but also the role activations, deliberative traces, aggregation rules, and feedback loops through which those outputs are produced. Our contribution is to reposition pluralistic alignment as a problem of socially grounded coordination rather than output diversification. We outline a design space for systems that engage multiple perspectives in structured and accountable ways, and we identify directions for future work to implement and empirically evaluate these approaches in real-world settings.