🤖 AI Summary
This study addresses the tendency in existing literature to reduce AI value alignment to a purely technical or normative issue, thereby overlooking its structural and governance dimensions. Drawing on principal–agent theory, the paper proposes a triaxial analytical framework encompassing goal specification, information distribution, and principal structure, systematically demonstrating for the first time that value alignment is fundamentally an institutional, pluralistic, and context-dependent governance challenge. By integrating institutional analysis with a multi-stakeholder perspective, the work clarifies that effective alignment requires dynamic trade-offs among diverse value systems. It further emphasizes the necessity of institutionalized processes to continuously recalibrate goal-setting mechanisms, evaluation protocols, and community engagement, thereby transcending purely technical approaches and advancing governance-oriented alignment practices.
📝 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.