  The classification of jets as quark- versus gluon-initiated is an important
yet challenging task in the analysis of data from high-energy particle
collisions and in the search for physics beyond the Standard Model. The recent
integration of deep neural networks operating on low-level detector information
has resulted in significant improvements in the classification power of
quark/gluon jet tagging models. However, the improved power of such models
trained on simulated samples has come at the cost of reduced interpretability,
raising concerns about their reliability. We elucidate the physics behind
quark/gluon jet classification decisions by comparing the performance of
networks with and without constraints of infrared and collinear safety, and
identify the nature of the unsafe information by revealing the energy and
angular dependence of the learned models. This in turn allows us to approximate
the performance of the low-level networks (by 99\% or higher) using equivalent
sets of interpretable high-level observables, which can be used to probe the
fidelity of the simulated samples and define systematic uncertainties.
