As law professors, we co-founders have always studied judicial decisions using quantitative methods,” says Benjamin Alarie, Osler Chair in Business Law at the University of Toronto and co-founder of Blue J Legal. “So I think we were primed for using machine learning and new algorithms to analyze judicial decision making.” Adding that he believes within five years it will be taken for granted that law firms will be using computational tools to get an initial take on a client situation or a legal research problem.

Alarie was serving as the Associate Dean of the Faculty of Law at U of T in January 2015 when he was asked to sit in as a judge for the IBM Watson Challenge, which took place at the university's computer science department.

“The students had a lot of interesting ideas for how to apply machine learning to law, but there was obviously a gap in legal expertise,” says Alarie. “I thought tax is such a fantastic area to use machine learning, because the tax system is so complex and deep. You have a third of GDP changing hands from the private to the public sector, so of course both sides want to optimize their behaviour.”

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