Relative Principals, Pluralistic Alignment, and the Structural Value Alignment Problem
2026-04-22 • Computers and Society
Computers and SocietyArtificial IntelligenceMultiagent Systems
AI summaryⓘ
The authors argue that AI alignment is not just about making sure AI follows the right rules technically, but about how decisions about AI goals, information access, and whose interests matter are managed. They use an economic idea called the principal-agent framework to explain that misalignment happens along three main areas: objectives, information, and principals (stakeholders). Their work shows alignment depends on governance — the systems and institutions deciding these aspects — rather than only on engineering AI models. They also highlight that alignment involves balancing different values and interests, meaning it requires ongoing management, not a one-time technical fix.
AI alignmentprincipal-agent frameworkgovernanceobjectivesinformation asymmetrystakeholdersmisalignmentinstitutional processesvalue pluralismtrade-offs
Authors
Travis LaCroix
Abstract
The value alignment problem for artificial intelligence (AI) is often framed as a purely technical or normative challenge, sometimes focused on hypothetical future systems. I argue that the problem is better understood as a structural question about governance: not whether an AI system is aligned in the abstract, but whether it is aligned enough, for whom, and at what cost. Drawing on the principal-agent framework from economics, this paper reconceptualises misalignment as arising along three interacting axes: objectives, information, and principals. The three-axis framework provides a systematic way of diagnosing why misalignment arises in real-world systems and clarifies that alignment cannot be treated as a single technical property of models but an outcome shaped by how objectives are specified, how information is distributed, and whose interests count in practice. The core contribution of this paper is to show that the three-axis decomposition implies that alignment is fundamentally a problem of governance rather than engineering alone. From this perspective, alignment is inherently pluralistic and context-dependent, and resolving misalignment involves trade-offs among competing values. Because misalignment can occur along each axis -- and affect stakeholders differently -- the structural description shows that alignment cannot be "solved" through technical design alone, but must be managed through ongoing institutional processes that determine how objectives are set, how systems are evaluated, and how affected communities can contest or reshape those decisions.