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

• URL: http://arxiv.org/abs/2604.20805v1
• Date: Wed, 22 Apr 2026 17:36:52 GMT
• Title: Relative Principals, Pluralistic Alignment, and the Structural Value Alignment Problem
• Abstract summary: I argue that the problem is better understood as a structural question about governance.<n>This paper reconceptualises misalignment as arising along three axes: objectives, information, and principals.
• Score: 0.20305676256390934
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Socially Grounded Agentic AI: Coordinating Plural Perspectives through Social Theory [4.253029474101735]
  We show how perspectives can be understood as structured by roles, shaped through interaction, and distributed across fields of power and expertise.<n>For agentic systems, this requires aligning not only final outputs, but also the role activations, deliberative traces, aggregation rules, and feedback loops through which those outputs are produced.
  arXiv  Detail & Related papers  (2026-08-04T16:37:09Z)
• A Survey of Reasoning in Autonomous Driving Systems: Open Challenges and Emerging Paradigms [49.66022971508878]
  We argue that reasoning should be elevated from a modular component to the system's cognitive core.<n>We derive and systematize seven core reasoning challenges, such as the responsiveness-reasoning trade-off and social-game reasoning.<n>We identify a fundamental and unresolved tension between the high-latency, deliberative nature of LLM-based reasoning and the millisecond-scale, safety-critical demands of vehicle control.
  arXiv  Detail & Related papers  (2026-03-11T07:40:53Z)
• Position: General Alignment Has Hit a Ceiling; Edge Alignment Must Be Taken Seriously [51.03213216886717]
  We take the position that the dominant paradigm of General Alignment reaches a structural ceiling in settings with conflicting values.<n>We introduce Edge Alignment as a distinct approach in which systems preserve multi dimensional value structure.
  arXiv  Detail & Related papers  (2026-02-23T16:51:43Z)
• From Educational Analytics to AI Governance: Transferable Lessons from Complex Systems Interventions [0.0]
  We argue that five core principles developed within CAPIRE transfer directly to the challenge of governing AI systems.<n>The isomorphism is not merely analogical: both domains exhibit non-linearity, emergence, feedback loops, strategic adaptation, and path dependence.<n>We propose Complex Systems AI Governance (CSAIG) as an integrated framework that operationalises these principles for regulatory design.
  arXiv  Detail & Related papers  (2025-12-15T12:16:57Z)
• Quantifying Misalignment Between Agents: Towards a Sociotechnical Understanding of Alignment [2.619545850602691]
  Recent sociotechnical approaches highlight the need to understand complex misalignment among multiple human and AI agents.<n>We adapt a computational social science model of human contention to the alignment problem.<n>Our model quantifies misalignment in large, diverse agent groups with potentially conflicting goals.
  arXiv  Detail & Related papers  (2024-06-06T16:31:22Z)
• Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making [69.44075077934914]
  "Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture. This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias. In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
  arXiv  Detail & Related papers  (2024-01-13T14:07:09Z)
• AI Alignment: A Comprehensive Survey [69.61425542486275]
  AI alignment aims to make AI systems behave in line with human intentions and values.<n>We identify four principles as the key objectives of AI alignment: Robustness, Interpretability, Controllability, and Ethicality.<n>We decompose current alignment research into two key components: forward alignment and backward alignment.
  arXiv  Detail & Related papers  (2023-10-30T15:52:15Z)
• Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness [55.037030060643126]
  Intersectionality is a critical framework that allows us to examine how social inequalities persist. We argue that adopting intersectionality as an analytical framework is pivotal to effectively operationalizing fairness.
  arXiv  Detail & Related papers  (2023-03-16T21:02:09Z)
• Causal Fairness Analysis [68.12191782657437]
  We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms. Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
  arXiv  Detail & Related papers  (2022-07-23T01:06:34Z)
• Steps Towards Value-Aligned Systems [0.0]
  Algorithmic (including AI/ML) decision-making artifacts are an established and growing part of our decision-making ecosystem. Current literature is full of examples of how individual artifacts violate societal norms and expectations. This discussion argues for a more structured systems-level approach for assessing value-alignment in sociotechnical systems.
  arXiv  Detail & Related papers  (2020-02-10T22:47:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.