Active Learning for Human-in-the-Loop Customs Inspection

We study the human-in-the-loop customs inspection scenario, where an AI-assisted algorithm supports customs officers by recommending a set of imported goods to be inspected. If the inspected items are fraudulent, the officers can levy extra duties. The updated decisions are used as additional training data for successive iterations. Inspecting only the likely fraudulent items may lead to an immediate gain in revenue, yet it does not bring new insights for learning dynamic trade patterns. In contrast, including uncertain items in the inspection helps gradually acquire new knowledge that will be used as supplementary training resources to update the system. Based on multiyear customs declaration logs obtained from three countries, we demonstrate that some degree of exploration is necessary to cope with domain shifts in trade data. The results show that a hybrid strategy of jointly selecting likely fraudulent and uncertain items will eventually outperform the exploitation-only strategy.