Explainable Product Classification for Customs
This addresses the need for efficient and interpretable product classification in customs operations, though it is incremental as it builds on existing classification tasks with a focus on explainability.
The authors tackled the problem of assigning commodity codes (HS codes) to traded goods for customs offices by developing an explainable decision-support model that suggests subheadings with reasoning. The model achieved 93.9% accuracy in top-3 suggestions for 925 challenging subheadings and reduced review time and effort for customs officers in a user study.
The task of assigning internationally accepted commodity codes (aka HS codes) to traded goods is a critical function of customs offices. Like court decisions made by judges, this task follows the doctrine of precedent and can be nontrivial even for experienced officers. Together with the Korea Customs Service (KCS), we propose a first-ever explainable decision supporting model that suggests the most likely subheadings (i.e., the first six digits) of the HS code. The model also provides reasoning for its suggestion in the form of a document that is interpretable by customs officers. We evaluated the model using 5,000 cases that recently received a classification request. The results showed that the top-3 suggestions made by our model had an accuracy of 93.9\% when classifying 925 challenging subheadings. A user study with 32 customs experts further confirmed that our algorithmic suggestions accompanied by explainable reasonings, can substantially reduce the time and effort taken by customs officers for classification reviews.