Prototype-Based Interpretability for Legal Citation Prediction
Chu Fei Luo Rohan Bhambhoria Samuel Dahan Xiaodan Zhu
Deep learning has made significant progress in the past decade, and
demonstrates potential to solve problems with extensive social impact. In
high-stakes decision making areas such as law, experts often require
interpretability for automatic systems to be utilized in practical settings. In
this work, we attempt to address these requirements applied to the important
problem of legal citation prediction (LCP). We design the task with parallels
to the thought-process of lawyers, i.e., with reference to both precedents and
legislative provisions. After initial experimental results, we refine the
target citation predictions with the feedback of legal experts. Additionally,
we introduce a prototype architecture to add interpretability, achieving strong
performance while adhering to decision parameters used by lawyers. Our study
builds on and leverages the state-of-the-art language processing models for
law, while addressing vital considerations for high-stakes tasks with practical
societal impact.