Adaptive Pluralistic Alignment: A pipeline for dynamic artificial democracy

arXiv cs.LG / 5/5/2026

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Key Points

The paper introduces Adaptive Pluralistic Alignment (APA), aiming to prevent AI “value lock-in” by letting aligned systems update as societal norms evolve over time.
APA is a modular three-stage pipeline that (1) learns compact personalized reward models using low-rank reward basis decomposition, (2) uses these models as a jury to select outputs via social-choice-theoretic voting, and (3) adapts the jury over time by updating annotator weights while keeping reward bases fixed.
The approach is designed to avoid repeating costly pretraining or large-scale data collection, while remaining efficient, explainable, steerable, and modular.
A proof-of-concept implementation using the PRISM multi-user alignment dataset with simulated historical annotators shows that jury composition and the voting rule can significantly change outcomes, especially with heterogeneous jury preferences.
The authors release full code and preference datasets for reproducibility via the provided repository link.

Abstract

Prevailing alignment methods target a fixed set of preferences and therefore risk forcing value lock-in as societal norms evolve over time. We introduce Adaptive Pluralistic Alignment (APA), a modular pipeline for updating pluralistically aligned AI systems to track evolving values and avoid value lock-in without repeating costly pretraining or large-scale data collection. APA has three stages: (1) learning compact personalized reward models via low-rank reward basis decomposition, (2) using these models as a jury that collectively selects among candidate outputs through social-choice-theoretic voting, and (3) efficiently adapting the jury over time by fitting new annotator weights over the fixed reward bases as values shift. The resulting system is efficient, explainable, steerable, and modular. We implement a proof-of-concept instantiation using the PRISM multi-user alignment dataset and simulated historical annotators, and provide preliminary analysis showing that jury composition and the choice of voting rule can substantially affect outcomes, particularly when jury preferences are heterogeneous. We provide full code and resulting preference datasets at https://anonymous.4open.science/r/apa.