Optimal resource allocation to minimize errors when detecting human trafficking
针对人口贩卖检测中资源有限且存在检测错误的问题，构建了一类资源分配模型，考虑不同最优分配场景，并在印度-尼泊尔边境和全球海产品行业验证了模型适用性，为反人口贩卖资源分配提供操作策略。
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.