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

TL;DR

This paper explores an active learning approach for human-in-the-loop customs inspection, balancing exploration and exploitation to improve detection of fraudulent goods and adapt to changing trade patterns.

Contribution

It introduces a hybrid inspection strategy combining fraud likelihood and uncertainty to enhance learning and revenue in customs inspections.

Findings

Hybrid strategy outperforms exploitation-only methods.

Exploration is essential to adapt to domain shifts.

Multiyear datasets validate the approach.

Abstract

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. Th formed logs are then used as additional training data for successive iterations. Choosing to inspect suspicious items first leads to an immediate gain in customs revenue, yet such inspections may not bring new insights for learning dynamic traffic patterns. On the other hand, inspecting uncertain items can help acquire new knowledge, which will be used as a supplementary training resource to update the selection systems. Based on multiyear customs datasets 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 selecting likely fraudulent and uncertain items will eventually outperform the exploitation-only strategy.