SOURCE-LINKED INTELLIGENCE

Algorithmic Impact Reveals the Hidden Social Choice Structure of Alignment

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

When an AI algorithm makes decisions that affect more than one person, aligning it becomes a problem of social choice: how should people's divergent preferences about system behavior be reconciled and aggregated into a single coherent model? The standard approach to aligning frontier AI models$\unicode{x2013}$reinforcement learning from human feedback$\unicode{x2013}$largely sidesteps this question and has poor social choice guarantees. However, it remains unclear what alternative should replace it. We show that, by focusing directly on an algorithm's welfare consequences, the alignment proble

Read original source ↗ Open in workspace

recordType

paper

region

Global