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This paper presents a novel framework for addressing the outer alignment problem in AIArtificial Intelligence (AI)-driven complex systemsComplex systems through an iterative evaluation process. The framework leverages systems theorySystems theory, emphasizing the significance of multi-level modelingModeling and the characterization of emergent behaviors in aligning system objectives with stakeholder-defined outcomes. By representing unknown and unobserved variables within emergent value functions, the framework accommodates the inherent complexityComplexity and unpredictability of AIArtificial Intelligence (AI) systems operating in dynamic environments. Inspired by the principles of dynamic systems theorySystems theory and neural network training, this approach iteratively refines system alignment through stakeholder feedback, reducing risks of misalignment and instability. The proposed methodology offers a generalizable structure to enhance system reliability, safety, and ethical alignment, with potential applications across diverse domains. The paper concludes by highlighting the theoretical advancements required to mathematically model emergent behaviors and validate alignment strategies.

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Dynamic Alignment Strategies for AI-Driven Systems: An Iterative Evaluation Framework

  • 会议论文

  • 发表日期：

    2026年

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  • Daniel Gossman,
  • Bryan Mesmer,
  • Hanumanthrao Kannan

摘要

This paper presents a novel framework for addressing the outer alignment problem in AIArtificial Intelligence (AI)-driven complex systemsComplex systems through an iterative evaluation process. The framework leverages systems theorySystems theory, emphasizing the significance of multi-level modelingModeling and the characterization of emergent behaviors in aligning system objectives with stakeholder-defined outcomes. By representing unknown and unobserved variables within emergent value functions, the framework accommodates the inherent complexityComplexity and unpredictability of AIArtificial Intelligence (AI) systems operating in dynamic environments. Inspired by the principles of dynamic systems theorySystems theory and neural network training, this approach iteratively refines system alignment through stakeholder feedback, reducing risks of misalignment and instability. The proposed methodology offers a generalizable structure to enhance system reliability, safety, and ethical alignment, with potential applications across diverse domains. The paper concludes by highlighting the theoretical advancements required to mathematically model emergent behaviors and validate alignment strategies.