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Here’s the introduction to this piece
Fellow Slaw columnist Omar Ha-Redeye recently wrote a blog entry on how artificial intelligence is making its way into the Canadian legal community more slowly that expected (by some) due to the fact that the data repositories that are behind SOQUIJ, CanLII, and other caselaw search engines are simply too limited in size to allow for true predictive capacities. In other words, there are too few decisions to generate reliable trends that can be identified through A.I. This has pushed Mr. Ha-Redeye to question how successful and useful a tool like Premonition will be on the Canadian market.
According to a press release announcing the arrival of the Premonition database in Canada:
“Premonition is an Artificial Intelligence system that mines Big Data to find out which Attorneys win before which Judges. According to the company, “It is a very, very unfair advantage in Litigation.” Premonition’s system can identify top performing Lawyers by win rate, case type, case duration and, most importantly, Judge. The company claims that the Attorney/Judge pairing to be worth 30.7% of case outcome on average. “The old saying that ‘A good lawyer knows the law, but a great one knows the Judge,’ is true,” says Guy Kurlandski, CEO.”
Notwithstanding the fact that “winning” is an extremely relative concept in litigation (having a court judge in your favour, but for half the amount in play, could be construed both as a win and a loss), data mining court decisions to retrieve usable statistics is but one way artificial intelligence can be used by lawyers and other stakeholders in the legal process.
Before going any further, however, we need to clarify a few intertwined concepts that can cause some confusion when studying the probable impacts of artificial intelligence on the legal process.
The first thing one needs to remember is that bots (i.e. software using A.I.) are not actual robots. When referring to “robot lawyers”, one shouldn’t imagine C-3P0 in a gown holding briefs, but rather a more intuitive version of CanLII or SOQUIJ. Secondly, artificial intelligence encompasses a variety of types of technologies that range from software using simple search algorithms to advanced applications (such as that behind IBM’s Watson), and beyond. Therefore, when the Merriam-Webster dictionary defines artificial intelligence as “the power of a machine to copy intelligent human behaviour”, it is, in our view, making a broad overgeneralisation and, we dare say, an ill-fated attempt to give human traits to software. We are reminded of the wise words written by Justice Mahoney in Apple Computer, Inc. v. Mackintosh Computers Ltd.:
“The principal difficulty which this case has given me arises from the anthropomorphic character of virtually everything that is thought or said or written about computers. Words like “language”, “memory”, “understand”, “instruction”, “read”, “write”, “command”, and many others are in constant use. They are words which, in their primary meaning, have reference to cognitive beings. Computers are not cognitive. The metaphors and analogies which we use to describe their functions remain just that.”
Machines are not really copying intelligent human behaviour, software is being created that can “learn” by comparing recently uploaded data with previous models and establishing commonalities. This is what is commonly referred to as machine learning. Therefore, even the most advanced forms of machine learning (often referred to as deep learning) are not really functioning like the human brain.
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The Use of Artificial Intelligence to Facilitate Settlements Through ODR
