Author ORCID Identifier
Sean Rehaag: 0000-0002-4432-9217
Document Type
Article
Publication Date
9-15-2025
Publisher
Access to Algorithmic Justic a2aj.ca
Abstract
The Access to Algorithmic Justice project (A2AJ) is an open-source alternative to the Canadian Legal Information Institute (CanLII). At a moment when technology promises to enable new ways of working with law, CanLII is becoming an impediment to the free access of law and access to justice movements because it restricts bulk and programmatic access to Canadian legal data. This means that Canada is staring down a digital divide: wellresourced actors have the best new technological tools and, because CanLII has disclaimed leadership, the public only gets second-rate tools. This article puts CanLII in its larger historical context and shows how long and deep eGorts to democratize access to Canadian legal data are, and how often they are thwarted by private industry. We introduce the A2AJ's Canadian Legal Data project, which provides open access to over 116,000 court decisions and 5,000 statutes through multiple channels including APIs, machine learning datasets, and AI integration protocols. Through concrete examples, we demonstrate how open legal data enables courts to conduct evidence-based assessments and allows developers to create tools for practitioners serving low-income communities.
Repository Citation
Wallace, Simon and Rehaag, Sean, "Access to Algorithmic Justice Working Paper: Introducing the A2AJ’s Canadian Legal Data: An Open-Source Alternative to CanLII for the Era of Computational Law" (Access to Algorithmic Justic a2aj.ca, 2025). Commissioned Reports, Studies and Public Policy Documents. Paper 269.
https://digitalcommons.osgoode.yorku.ca/reports/269
Included in
Legal Profession Commons, Legal Writing and Research Commons, Science and Technology Law Commons
Comments
"This article draws on research supported by the Social Sciences and Humanities Research Council and the Law Foundation of Ontario. Some sections of the article, as well as some parts of the underlying code used for the project, were drafted with the assistance of generative AI."