The Limits of AI-Driven Allocation: Optimal Screening under Aleatoric Uncertainty

TL;DR

This paper examines the fundamental limits of AI-driven resource allocation under irreducible uncertainty, proposing an optimal two-stage targeting strategy that combines screening and risk-based allocation.

Contribution

It introduces a theoretical framework for combining screening and algorithmic targeting, highlighting when they act as complements or substitutes under aleatoric uncertainty.

Findings

Optimal screening occurs at the margin of algorithmic allocation.

Directly targeting high-risk units maximizes efficiency.

Screening benefits increase with higher aleatoric uncertainty.

Abstract

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. Yet, even access to the true conditional vulnerability probability cannot eliminate misallocation: aleatoric uncertainty over individual vulnerability status is irreducible, and probabilistic targeting inevitably misallocates some resources. In this work we study how screening and algorithmic targeting should be optimally combined in a two-stage allocation framework where a screening stage observes true outcomes for a subset of units before a final allocation stage assigns the resource under a fixed coverage budget. We show that the optimal strategy screens units at the margin of algorithmic allocation, while directly targeting the highest-risk units. Furthermore, we empirically characterize when screening and algorithmic targeting act as complements or substitutes: efficiency gains from screening grow as the aleatoric uncertainty in the population increases. We illustrate our framework with applications in income-based social protection programs and humanitarian demining in Colombia, where the tension between screening costs and allocation efficiency is operationally consequential.