Paper Overview
Research area: Machine Learning Author: Travis LaCroix Published: 2026-04-22 arXiv: 2604.20805
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. The author argues 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, the 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 as an outcome shaped by how objectives are set, how information is distributed, and whose interests are counted in practice.
The paper's central contribution is showing that this three-axis decomposition implies alignment is fundamentally a governance problem, not merely an engineering one. From this perspective, alignment is inherently pluralistic and context-dependent, and addressing alignment failures involves trade-offs between competing values.
Because alignment failures can occur along each axis — and affect different stakeholders in different ways — the structural analysis suggests that alignment cannot be 'solved' through technical design alone. Instead, it must be managed through ongoing institutional processes that determine how objectives are set, how systems are evaluated, and how affected communities are permitted to contest or reshape those decisions.
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