arXiv:2605.01642v2 Announce Type: replace 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 am
The paper directly addresses the evolving challenge of 'value lock-in' in AI alignment, a critical concern as AI systems become more pervasive and influential in society.
This development offers a potential solution for creating AI systems that can adapt to changing societal norms, preventing ossification of values and enabling more equitable and dynamic AI governance.
AI alignment strategies could shift from static preference targeting to dynamic, pluralistic adaptation, reducing the need for costly retraining and promoting continuous value evolution.
- · AI developers
- · Governments/Regulators
- · Social ethicists
- · AI-influenced industries
- · Rigid AI alignment frameworks
- · Entities reliant on fixed AI value systems
AI systems will become more capable of reflecting and evolving with diverse human values, reducing potential societal friction.
This could lead to new models of computational democracy or collective intelligence, where AI acts as an adaptive facilitator rather than a fixed decision-maker.
The ability to dynamically align AI with pluralistic values might mitigate existential risks associated with misaligned AGI, fostering greater public trust and broader adoption.
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Read at arXiv cs.LG