  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.
