ICAIL 2019 In Review

Author:Jason Morris
Date:August 08, 2019

The International Association for Artificial Intelligence and Law (IAAIL) is a non-profit organization that has organized academic conferences on artificial intelligence and law every two years, since 1987.

The most recent International Conference on AI and Law (ICAIL) was held this June in Montréal, QC, hosted by the CyberJustice Lab at the University of Montréal Faculty of Law. I had the opportunity to attend, presenting the results of my work as part of the ABA Innovation Fellowship program in 2018/2019, which was sponsored by Canadian legal practice management software company, Clio.

The conference is an academic event. It features people who are computing academics, people who are legal academics, and people who are both. Among attendees there was a considerable contingent of practicing lawyers, and software companies that sell AI services to practicing lawyers.

With the conference a few weeks in the rear-view mirror, here are a few thoughts that stand out for me today.

“Explainability” is the Brass Ring

Right now, in the academic AI and law community, the next big goal is explainability. Tools that can make predictions using machine learning techniques and good data are no longer news. But the techniques we have can’t explain or justify those predictions in the way that the legal system anticipates. Techniques for explanation or justification are a very active area of research across all kinds of AI, but in Law and AI they are a major preoccupation.

Older, rule-based AI techniques have a great deal of explainability built in, but they have challenges in terms of power and usability for legal practitioners. There were people at the conference trying to solve those problems in innovative ways, too, and they have made impressive progress.

In Law, “AI” and “Machine Learning” are Not the Same Thing

Speaking of older AI techniques, in almost any other context, you would expect an AI conference to be swamped with people taking about machine learning, reinforcement learning, deep learning, adversarial systems, and all of the data-based AI techniques that have become what people think of as AI.

But it seems that the legal problem domain is different. Machine learning approaches were discussed in only about half of the conference papers. A surprisingly large part of the conversation at ICAIL was about rule-based reasoning techniques, ontology, and similar methods that would have been familiar ground for the people who presented at the inaugural...

To continue reading