  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.
