Empowering Refugee Claimants and their Lawyers: Using Machine Learning to Examine Decision-Making in Refugee Law

TL;DR

This paper explores using NLP and machine learning to improve decision-making, transparency, and efficiency in refugee status adjudications by analyzing legal cases and developing a new benchmark for NLP in refugee law.

Contribution

It introduces a novel NLP benchmark for refugee law and demonstrates the feasibility of automating case retrieval and decision analysis processes.

Findings

Successful experiment on case retrieval

Feasibility of automating decision analysis steps

Introduction of a new NLP benchmark for refugee law

Abstract

Our project aims at helping and supporting stakeholders in refugee status adjudications, such as lawyers, judges, governing bodies, and claimants, in order to make better decisions through data-driven intelligence and increase the understanding and transparency of the refugee application process for all involved parties. This PhD project has two primary objectives: (1) to retrieve past cases, and (2) to analyze legal decision-making processes on a dataset of Canadian cases. In this paper, we present the current state of our work, which includes a completed experiment on part (1) and ongoing efforts related to part (2). We believe that NLP-based solutions are well-suited to address these challenges, and we investigate the feasibility of automating all steps involved. In addition, we introduce a novel benchmark for future NLP research in refugee law. Our methodology aims to be inclusive to all end-users and stakeholders, with expected benefits including reduced time-to-decision, fairer and more transparent outcomes, and improved decision quality.