AI Alignment From Social Choice Perspectives
Alignment from human feedback uses human judgments about model outputs to steer the behavior of language models after pretraining. When those judgments reflect conflicting views of desirable behavior, the learned objective becomes an aggregate determination of what the model should prefer. We survey recent work that has studied this aggregation problem through the lens of social choice theory. We illustrate how the social choice perspective helps identify failure modes in the feedback aggregation layer and reveals a broader design space for handling disagreement in explicit and principled ways.
| Authors | Daniel Halpern, Evi Micha, Ariel D. Procaccia et al. |
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| Primary category | cs.AI |
| Published | 19 June 2026 |
| Last updated at source | 15 October 2026 |
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