基于社会基础的智能体人工智能：通过社会理论协调多元视角
Socially Grounded Agentic AI: Coordinating Plural Perspectives through Social Theory
浏览论文内容
中文总结 AI 辅助
本文提出将社会理论用于人工智能系统设计，以解决多元对齐问题，将多元对齐重新定位为基于社会基础的协调问题，勾勒了相关系统的设计空间并指明未来研究方向。
中文摘要 AI 辅助
随着人工智能系统被部署到日益多样化的社会场景中，对齐问题不再能被表述为对单一、统一价值观集合的优化，相反，系统必须能够识别、表征并回应多种合法的视角，这使得多元对齐的研究兴趣日益增长，该研究旨在超越一刀切的适当行为模型。然而，当前方法往往缺乏对价值观在实践中如何被社会地组织、争议和协调的清晰阐释。本文中，我们认为社会理论为应对这些挑战提供了关键的概念和设计资源。借鉴社会学的既有传统，我们展示了视角如何被角色结构化、通过互动塑造，并分布在权力与专业领域中。我们将这些见解转化为人工智能系统设计的具体启示，包括基于角色的表征、视角间的结构化协调以及情境敏感的评估。对于智能体系统而言，这不仅需要对齐最终输出，还需要对齐产生这些输出的角色激活、审议轨迹、聚合规则和反馈回路。我们的贡献在于将多元对齐重新定位为基于社会基础的协调问题，而非输出多样化问题，我们勾勒了以结构化且可问责的方式处理多元视角的系统设计空间，并确定了未来在现实场景中实施和实证评估这些方法的研究方向。
英文摘要
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.
