AI RESEARCH
The Limits of AI-Driven Allocation: Optimal Screening under Aleatoric Uncertainty
arXiv CS.AI
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ArXi:2605.07979v1 Announce Type: new The rise of machine learning has shifted targeted resource allocation in policy and humanitarian settings toward algorithmic targeting based on predicted risk scores. This approach is typically cheaper and faster than traditional screening procedures that directly observe the latent vulnerability status through physical verification.