Preference Elicitation for Policy Optimization and Application to Aligning Heart Transplantation with Human Values
Preference elicitation is essential for aligning AI systems with human values. Prior approaches (e.g., for organ allocation) often ask stakeholders to compare the decisions of an algorithm (e.g., patient A vs. patient B). Such a decision-level approach conflates the means with the ends. Instead, we elicit preferences directly over allocation outcomes to learn a utility function for policy optimization. We construct a novel preference elicitation algorithm for linear utilities that outperforms prior techniques in practice. Our algorithm has two phases. The first phase learns cutting planes through pairwise comparisons to rapidly shrink the space of possible attribute weights and warm-start...
| Authors | Itai Zilberstein, Ioannis Anagnostides, Zachary W Sollie et al. |
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| Primary category | cs.AI |
| Published | 29 July 2026 |
| Last updated at source | 9 October 2026 |
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