Render unto lawyers that which is lawyers’, and unto support tools the things that are support tools


Render unto lawyers that which is lawyers’, and unto support tools the things that are support tools’

I would like to start with a trite but potentially provocative observation: a great many legal matters do not require a lawyer to resolve. The future of legal practice is one where we do better at triaging legal services. By triaging I mean the process of parsing which services require a lawyer for resolution, and which do not. At the Cyberjustice Lab, we have developed a tool called JusticeBot, which answers questions about tenancies that might otherwise require a lawyer to answer. JusticeBot does not provide legal advice — instead it is a solution explorer, which uses Artificial Intelligence (“AI”) to guide you through the experience of being a tenant or landlord with a legal issue, and provides possible answers to your legal issue(s).

The future of legal practice in Canada involves triaging legal services into those that need lawyers and those that can use legal self-help tools like online resources, solution explorers, case predictors, and other free, quick, and readily accessible tools. This will have innumerable benefits for the access to justice crisis in Canada. At the Cyberjustice Lab, we are trying to change practice by allowing individuals to have more control over their own legal destiny by educating themselves and empowering them to resolve, where appropriate, their legal issues without lawyers.

Technology is part of the future of legal practice — and part of the solution to the access to justice crisis. At the Cyberjustice Lab, we are harnessing recent improvements in AI and Natural Language Processing (“NLP”) to make our legal self-help tool, JusticeBot, more effective. NLP is a kind of computer programme that allows us to make sense of messy data — from cases to transcripts to emails and more. NLP allows us at the Cyberjustice Lab, for example, to allow an individual to type in their tenancy related legal issue into JusticeBot — NLP is able to “read” the typed text and guide them down a set pathway to their likely legal solution.

Technological improvements have been substantial in recent years — AI backed solution explorers are only one such case. NLP is another. NLP can help reduce legal costs and can provide legal insights that would otherwise require thousands of hours of legal work. In one recent study, I supervised a team of three Masters of Data Science students at UBC to develop a NLP protocol that “read” every single negligence case in the BC Supreme Court from 2000-2020 — roughly 4400 cases, a feat that took these relatively novice student programmers a few hundred hours — not thousands. Using nothing more complex than these students’ intelligence and grit, and a decent amount of coding, we were able to extract that the written length of cases in this time had nearly tripled, damage awards had increased from $150,000 on average to $225,000 (adjusted for inflation) and contributory negligence had stayed static at around 20% of cases. The implications of NLP for the future of legal practice is profound: if cases — the bread and butter of legal practice — can be interpreted and mined en masse by NLP, then, with appropriate triaging, the domains where lawyers have exclusive jurisdiction shrinks further, and purpose-built online legal tools can operate in this ceded jurisdiction.

At the Cyberjustice Lab, we are part of an emerging access to justice movement that operates in the space where formal legal advice is only one of the potential solutions to the issue — and with the radical improvements of AI, NLP, and other technologies, this space is only going to get larger and larger. We are part of the future of legal practice. This future involves effective distribution of scarce resources and judicious use of legal work — as well as using technology to resolve those issues where a lawyer’s involvement is surplus to requirements.