Socially Grounded Agentic AI: Coordinating Plural Perspectives through Social Theory
A paper published on arXiv on 2026-08-04 argues that pluralistic alignment of AI systems requires social theory to structure values by roles, interaction, and power, and proposes role-based representations, structured coordination, and context-sensitive evaluation for agentic systems.
As AI systems are deployed in diverse social contexts, alignment cannot rely on a single set of values. The paper advocates for pluralistic alignment, using sociological insights to design agentic AI that recognizes multiple perspectives through role-based representations, structured coordination, and context-sensitive evaluation, extending alignment to role activations, deliberative traces, and aggregation rules.
The paper translates social theory into concrete design implications: role-based representations encode perspectives tied to social roles; structured coordination mechanisms manage interactions among perspectives; context-sensitive evaluation assesses outputs based on social context. For agentic systems, alignment must cover not only final outputs but also role activations, deliberative traces, and aggregation rules.
This research signals a shift from monolithic value alignment to systems that can navigate pluralistic social environments, potentially enabling AI agents to operate more effectively in multi-stakeholder settings such as healthcare, governance, and collaborative platforms.
Pluralistic alignment can reduce risks of value misalignment in enterprise and consumer AI, improving trust and adoption in applications requiring coordination among diverse user groups, such as content moderation, personalized assistants, and collaborative decision-making tools.
Observable next signals include the development of role-based agent architectures, benchmarks for pluralistic alignment, and integration of sociological models into agentic AI frameworks. Adoption may be driven by regulatory demands for fairness and transparency in high-stakes domains.