Prototype-Based Interpretability for Legal Citation Prediction
This work addresses the need for interpretable AI in high-stakes legal decision-making, though it appears incremental as it builds on existing language models for law.
The paper tackled the problem of legal citation prediction by designing a task that mirrors lawyers' thought processes and introducing a prototype architecture for interpretability, achieving strong performance while adhering to legal decision parameters.
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.