Interpretable Low-Resource Legal Decision Making. (arXiv:2201.01164v1 [cs.LG])

Over the past several years, legal applications of deep learning have been on
the rise. However, as with other high-stakes decision making areas, the
requirement for interpretability is of crucial importance. Current models
utilized by legal practitioners are more of the conventional machine learning
type, wherein they are inherently interpretable, yet unable to harness the
performance capabilities of data-driven deep learning models. In this work, we
utilize deep learning models in the area of trademark law to shed light on …

arxiv decision decision making legal making