Socially Grounded Agentic AI: Coordinating Plural Perspectives through Social Theory
Building AI systems that balance multiple viewpoints instead of enforcing one worldview
When AI systems make decisions affecting diverse communities, treating everyone's values as identical doesn't work. This paper argues that AI designers should use social theory—studying how people actually organize, contest, and coordinate different perspectives in the real world—to build systems that recognize and respond to multiple legitimate viewpoints rather than flattening them into a single "correct" answer.
AI increasingly makes decisions in contexts where different groups have genuinely conflicting but reasonable values: who counts as creditworthy, what counts as harmful speech, how to balance privacy against safety. Systems built on a single unified value set will predictably anger or harm people whose legitimate perspective was never represented. Using social theory to design AI that explicitly tracks roles, expertise, and power dynamics means decisions can be contested and explained in ways people actually recognize from their own communities.