AI Alignment From Social Choice Perspectives

The paper presents a novel approach to AI alignment by applying social choice theory to the aggregation of human feedback for language models. It highlights how conflicting human judgments can complicate the learned objectives, and discusses potential failure modes in the feedback aggregation process. This perspective offers insights into designing more robust systems that can effectively manage disagreements in feedback, which is crucial for practitioners aiming to improve alignment in AI systems.