AINeutralarXiv – CS AI · 6h ago6/10
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Repair Before Veto: Repair-Augmented Constraint Learning for Contextual Decisions
Researchers introduce Repair-Augmented Constraint Learning (RACL), a machine learning framework that decides whether to repair constraint violations before rejecting candidates, rather than applying hard vetoes immediately. The method achieves significantly lower false-veto rates (0.25%) compared to baseline approaches (26.4%) on real-world airline data, with applications to automated decision systems.