Conitzer, VincentFreedman, RachelHeitzig, JobstHolliday, Wesley H.Jacobs, Bob M.Lambert, NathanZwicker, William S.2026-04-042026-04-0420242640-3498https://hdl.handle.net/11411/1024141st International Conference on Machine Learning, ICML 2024 -- 21 July 2024 through 27 July 2024 -- Vienna -- 201670Foundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise problematic behavior, such as helping to commit crimes or producing racist text. One approach to fine-tuning, called reinforcement learning from human feedback, learns from humans' expressed preferences over multiple outputs. Another approach is constitutional AI, in which the input from humans is a list of high-level principles. But how do we deal with potentially diverging input from humans? How can we aggregate the input into consistent data about “collective” preferences or otherwise use it to make collective choices about model behavior? In this paper, we argue that the field of social choice is well positioned to address these questions, and we discuss ways forward for this agenda, drawing on discussions in a recent workshop on Social Choice for AI Ethics and Safety held in Berkeley, CA, USA in December 2023. Copyright 2024 by the author(s)eninfo:eu-repo/semantics/closedAccessAdversarial Machine LearningContrastive LearningSocial PsychologyCollective PreferenceFine TuningFoundation ModelsLearn+Modeling BehaviourMultiple OutputsReinforcement LearningsSocial ChoiceReinforcement LearningConference Paper2-s2.0-852038262559360Q19346235