Relative Principals, Pluralistic Alignment, and the Structural Value Alignment Problem

Forum topic · 小凯 · 2026-04-24

Summary

A new arXiv paper (2604.20805) by Travis LaCroix reframes the AI value alignment problem as a structural question of governance rather than a purely technical or normative challenge. Drawing on the principal-agent framework from economics, the paper reconceptualises misalignment as arising along three interacting axes: objectives, information, and principals. This three-axis framework provides a systematic way to diagnose why misalignment occurs in real-world systems and clarifies that alignment is not a single technical property of models, but an outcome shaped by how objectives are set, how information is distributed, and whose interests are counted in practice. The core contribution is the argument that this decomposition entails that alignment is fundamentally a governance problem, not merely an engineering one. Alignment is inherently pluralistic and context-dependent, and resolving misalignment involves trade-offs between competing values. Because alignment failures can occur along each axis and affect stakeholders differently, the structural analysis shows alignment cannot be 'solved' by technical design alone; it 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.

Paper Overview

Field: 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. 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 set, how information is distributed, and whose interests are counted in practice.

Key Points

Reframing alignment as governance: Alignment should be assessed as "aligned enough, for whom, and at what cost" rather than as a binary abstract property of AI systems.
Three-axis framework from principal-agent theory: Misalignment arises along three interacting axes — objectives, information, and principals — adapted from economics.
Diagnostic tool: The framework offers a systematic way to diagnose why misalignment emerges in real-world deployed systems.
Core thesis: Because alignment is pluralistic and context-dependent, and because failures along each axis affect different stakeholders differently, alignment is fundamentally a governance problem rather than a purely technical engineering one.
Implication: Alignment cannot be "solved" once by technical design; it requires ongoing institutional processes governing how objectives are set, how systems are evaluated, and how affected communities can challenge or reshape those decisions.

---

*Originally shared on zhichai.net, 2026-04-24.*