Abstract

Free legal assistance is critically under-resourced, and many of those who seek legal help have their needs unmet. A major bottleneck in the provision of free legal assistance to those most in need is the determination of the precise nature of the legal problem. This paper describes a collaboration with a major provider of free legal assistance, and the deployment of natural language processing models to assign area-of-law categories to real-world requests for legal assistance. In particular, we focus on an investigation of models to generate efficiencies in the triage process, but also the risks associated with naive use of model predictions, including fairness across different user demographics.

Highlights

  • In addition to the issues surrounding the inherent fuzziness of legal categories, the descriptions ofThe number of Australians with unmet legal needs is estimated to be over 4 million people per year and growing: each year approximately 8.5 million Australians will have a legal problem and only around 4.5 million will access any assistance (Coumarelos et al, 2012; The Department of Justice and Regulation, 2012) — an indication that free legal assistance services are critically under-resourced

  • We investigate the viability of semi-automating this step by building a model that suggests how to categorise lay descriptions of problems/incidents into legal areas

  • It is critical that we develop models which will perform well for users of all backgrounds, generalise well from small amounts of curated data, and potentially dynamically interact with the help-seeker to clarify the nature of the case

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Summary

Introduction

In addition to the issues surrounding the inherent fuzziness of legal categories, the descriptions of. It is critical that we develop models which will perform well for users of all backgrounds, generalise well from small amounts of curated data, and potentially dynamically interact with the help-seeker to clarify the nature of the case. Can overcome these biases for future iterations of the model while keeping in mind the protection and 3 Data Set Development privacy of the help-seekers who are most vulnerable

Background
Legal services also experience this issue as a
AREA OF LAW
Findings
SYSTEM p r f

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