AI Alignment From Social Choice Perspectives
The paper analyzes AI alignment from human feedback as a formal aggregation pipeline using social choice theory. It shows how this framework helps identify failure modes when annotators disagree, such as issues with reward learning robustness and unanimity failures. The work suggests a broader design space for handling conflicting human preferences in alignment methods.