Abstract
Much alignment work assumes that there is a single set of human preferences or values that AI systems should align to. Pluralistic alignment, on the other hand, is concerned with integrating diverse human values and perspectives into alignment algorithms and evaluations. In this talk, I will start by presenting our roadmap to pluralistic alignment, offering definitions and scaffolding for research in the area. I will present one proposed algorithmic framework to support pluralism through modular specialist systems, along with a dataset and system to improve computational value-modeling. I will conclude with future research directions and open questions in the area.