Access to justice remains a global challenge, with many citizens still finding it difficult to seek help from the justice system when facing legal issues. Although the internet provides abundant legal information and services, navigating complex websites, understanding legal terminology, and filling out procedural forms continue to pose barriers to accessing justice. This paper introduces the LegalWebAgent framework that employs a web agent powered by multimodal large language models to bridge the gap in access to justice for ordinary citizens. The framework combines the natural language understanding capabilities of large language models with multimodal perception, enabling a complete process from user query to concrete action. It operates in three stages: the Ask Module understands user needs through natural language processing; the Browse Module autonomously navigates webpages, interacts with page elements (including forms and calendars), and extracts information from HTML structures and webpage screenshots; the Act Module synthesizes information for users or performs direct actions like form completion and schedule booking. To evaluate its effectiveness, we designed a benchmark test covering 15 real-world tasks, simulating typical legal service processes relevant to Québec civil law users, from problem identification to procedural operations. Evaluation results show LegalWebAgent achieved a peak success rate of 86.7%, with an average of 84.4% across all tested models, demonstrating high autonomy in complex real-world scenarios.

Interacting with the legal system and the government requires the assembly and analysis of various pieces of information that can be spread across different (paper) documents, such as forms, certificates and contracts (e.g. leases). This information is required in order to understand one's legal rights, as well as to fill out forms to file claims in court or obtain government benefits. However, finding the right information, locating the correct forms and filling them out can be challenging for laypeople. Large language models (LLMs) have emerged as a powerful technology that has the potential to address this gap, but still rely on the user to provide the correct information, which may be challenging and error-prone if the information is only available in complex paper documents. We present an investigation into utilizing multi-modal LLMs to analyze images of handwritten paper forms, in order to automatically extract relevant information in a structured format. Our initial results are promising, but reveal some limitations (e.g., when the image quality is low). Our work demonstrates the potential of integrating multi-modal LLMs to support laypeople and self-represented litigants in finding and assembling relevant information.

In this article, we introduce LLMediator, an experimental platform designed to enhance online dispute resolution (ODR) by utilizing capabilities of state-of-the-art large language models (LLMs) such as GPT-4. In the context of high-volume, low-intensity legal disputes, alternative dispute resolution methods such as negotiation and mediation offer accessible and cooperative solutions for laypeople. These approaches can be carried out online on ODR platforms. LLMediator aims to improve the efficacy of such processes by leveraging GPT-4 to reformulate user messages, draft mediator responses, and potentially autonomously engage in the discussions. We present and discuss several features of LLMediator and conduct initial qualitative evaluations, demonstrating the potential for LLMs to support ODR and facilitate amicable settlements. The initial proof of concept is promising and opens up avenues for further research in AI-assisted negotiation and mediation.

Nearly a decade after co-founding Cyberjustice Laboratory, a unique hub that analyses the impact of technologies on justice while developing concrete technological tools that are adapted to the reality of justice systems, Karim Benyekhlef and Fabien Gélinas have set their sights on artificial intelligence. The Autonomy through Cyberjustice Technologies (ACT), the latest brainchild of the Cyberjustice Laboratory, is the largest international multidisciplinary research initiative that seeks to leverage artificial intelligence to increase access to justice while providing justice stakeholders with a roadmap to help them develop technology that is better adapted to justice. "The main objective behind the initiative is to ensure that individuals know their rights, understand their legal situation regarding their problems and improve access to justice – and AI may help accomplish those goals," said Benyekhlef, the head of Cyberjustice Laboratory and a law professor at the Université de Montréal. "There's a good chance that our reflections and work on areas such as privacy, data management, data governance could easily be used in other realms such as in public administration. But we must be careful. We cannot play the sorcerer's apprentice. These are tools that are not yet mature. There's work to be done."