Getting In Contract with Large Language Models -- An Agency Theory Perspective On Large Language Model Alignment

2025-09-09

AI Summary

Large language models (LLMs) deployed in organizational settings suffer from alignment failures—such as goal misgeneralization and discriminatory outputs—due to their black-box nature and information asymmetry during adoption; existing research lacks a systematic integration of organizational adoption processes with AI alignment mechanisms. Method: Drawing on principal-agent contract theory, this paper introduces the first theoretically grounded, end-to-end alignment framework for organizational LLM deployment, spanning “identification–evaluation–deployment–governance” stages. Through conceptual literature analysis, it develops the LLM ATLAS framework, which formally classifies stage-specific contractual governance mechanisms and alignment strategies. Contribution/Results: LLM ATLAS bridges the cognitive gap between organizational practitioners and LLM agents, establishing a rigorous theoretical foundation and actionable pathway for accountable, auditable, organization-level LLM governance. It advances both alignment science and organizational AI adoption theory by unifying technical alignment with institutional contract design.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: (Large) Language ModelsMultiagent Systems: Mechanism Design

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSearch and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved informationUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Adopting Large language models (LLMs) in organizations potentially revolutionizes our lives and work. However, they can generate off-topic, discriminating, or harmful content. This AI alignment problem often stems from misspecifications during the LLM adoption, unnoticed by the principal due to the LLM's black-box nature. While various research disciplines investigated AI alignment, they neither address the information asymmetries between organizational adopters and black-box LLM agents nor consider organizational AI adoption processes. Therefore, we propose LLM ATLAS (LLM Agency Theory-Led Alignment Strategy) a conceptual framework grounded in agency (contract) theory, to mitigate alignment problems during organizational LLM adoption. We conduct a conceptual literature analysis using the organizational LLM adoption phases and the agency theory as concepts. Our approach results in (1) providing an extended literature analysis process specific to AI alignment methods during organizational LLM adoption and (2) providing a first LLM alignment problem-solution space.

Problem

• Addressing AI alignment issues in organizational LLM adoption
• Mitigating information asymmetries between adopters and black-box LLMs
• Developing agency theory-based framework for LLM alignment

Innovation

• Agency theory-based framework for LLM alignment
• Addresses information asymmetry in organizational adoption
• Conceptual solution space for AI alignment problems