Optimal resource allocation to minimize errors when detecting human trafficking

Abhishek Ray, Viplove Arora, Kayse Maass and Mario Ventresca

IISE Transactions, 2024, vol. 56, issue 3, 325-339

Abstract: Accurately detecting human trafficking is particularly challenging due to its covert nature, difficulty in distinguishing trafficking from non-trafficking exploitative conditions, and varying operational definitions. Typically, detecting human trafficking requires resource-intensive efforts from resource-constrained anti-trafficking stakeholders. Such measures may need personnel training or machine learning-based identification technologies that suffer from detection errors. Repeated usage of such measures risks biasing detection efforts and reducing detection effectiveness. Such problems raise the question: “How should imperfect detection resources be allocated to most effectively identify human trafficking?” As an answer, we construct a class of resource allocation models that considers various optimal allocation scenarios. These scenarios range from optimal location selection for monitoring to optimal allocation of a finite set of imperfect resources, given error rates. We illustrate the applicability of these models across both human and technology-facilitated detection contexts at the India–Nepal border and in the global seafood industry. Insights from our models help inform operational strategies for allocating limited anti-human trafficking resources in a way that effectively preserves human rights and dignity.

Date: 2024
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DOI: 10.1080/24725854.2023.2177364

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