Computer Science > Machine Learning
[Submitted on 27 Oct 2020 (v1), last revised 23 Feb 2022 (this version, v3)]
Title:Active Learning for Human-in-the-Loop Customs Inspection
View PDFAbstract: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.
Submission history
From: Sundong Kim [view email][v1] Tue, 27 Oct 2020 13:31:31 UTC (1,449 KB)
[v2] Mon, 17 Jan 2022 07:23:55 UTC (2,223 KB)
[v3] Wed, 23 Feb 2022 08:40:56 UTC (2,223 KB)
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