Static vs Dynamic Alignment

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FIG Fellows: Gracie Green

Authors: Gracie Green

Abstract:

This paper examines a critical distinction in AI alignment approaches: static alignment, where AI models maintain fixed goals from their training period, vs dynamic alignment, where models continuously update to match current human preferences. I argue that dynamic alignment, which allows AI systems to adapt to evolving human values, presents significant advantages over static alignment despite still facing many challenges. The analysis focuses on three key areas: the impact of particular alignment styles on model behaviour, the implications for human autonomy and the privacy concerns inherent in preference monitoring. By examining how these models interact with human agency and value development, I demonstrate that dynamic alignment better preserves human autonomy while raising important privacy considerations. The paper also explores technical challenges specific to dynamic alignment, including the difficulty of accurately interpreting human preferences and protecting against value manipulation. While dynamic alignment appears theoretically preferable, significant research is needed to ensure its practical implementation preserves both autonomy and privacy. This work concludes by identifying critical areas for future research in developing and verifying dynamic alignment systems.