Lexpert US Guides

2018 Lexpert US Guide

The Lexpert Guides to the Leading US/Canada Cross-Border Corporate and Litigation Lawyers in Canada profiles leading business lawyers and features articles for attorneys and in-house counsel in the US about business law issues in Canada.

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www.lexpert.ca/usguide | LEXPERT • June 2018 | 7 wrote, "e art and science of merger execution have made great strides since the late 1990s — a period when stock-market frenzy oen led to a rush to judgment, and ultimately to buyer's remorse. Since then, a more prudent, systematic approach to mergers and acquisitions has emerged," and there is much discussion of strategic due diligence. Add the volumes of correspon- dence data and other data generated within corporations and it can be said that AI is a remedy to this build-up. It's a computerized response to a comput- erized problem. According to an April 2017 AI Legal Tech conference paper, "AI is an overarching term that includes many branches and sub-sets of technol- ogies. However, the most common forms of AI in the legal tech sector are Machine Learning, Deep Learning, and Natural Language Processing." Deep Learning is used by a computer "to understand legal language, then compare the language with other contracts to identify boilerplate vs. custom, measure the complexity and readability of the language, and identify the responsibili- ties, rights, and terms of an agreement," according to Legal Robot's website. Natural Language Processing is "needed for data mining of external 'big data' sources and for addressing the legacy contract encoding problem," wrote Pierre Mitchell on the Spend Matters website. "e latter is the biggest short- term problem, especially if [the law firm or department] has grown through acquisition or has not been rigorous in establishing a standardized clause library. But there are contract analytics provid- ers (tools and/or services) that can help depending on your situation. It's a thorny issue because of the dense 'legalese' written in various languages (which have multiple semantic and legal interpretations) that must be classified and mined to extract the atomic-level insights about obligations, rights and risks of interest. is NLP of 'natural' commercial language can't be scalably addressed by human coded, rules- based approaches." Machine Learning "refers to comput- ers that 'learn' from the data they process rather than relying on humans for rules- based procedural programming to act upon that data. It not only discovers patterns in data but also specifically helps correlate various data inputs and key data outputs, which helps enable predictive analytics," Mitchell wrote. But humans are still needed to take machine learning forward, Mitch- ell added. "In a 'supervised learning' approach, human experts determine the outputs and the system 'learns' how to mimic the human experts, as well as uncover latent variables and inter- actions that humans wouldn't have spotted on their own. 'Unsupervised learning' doesn't rely on humans for direct training and stretches into the realm of deep learning." For all of AI's promise, the language barrier between lawyers and comput- ers inhibits it, but this is changing as avant-garde lawyers learn coding. ere is arguably a level of fear about being replaced or at least eclipsed holding lawyers back from learning more about computer language. Senior lawyers who do not themselves execute on due diligence anymore may be content to have juniors continue to do it as a learning exercise. But what do students and associates who are executing on manual due diligence think? Noah Waisberg le his early Big Law career to establish an AI due diligence company. Zach Abramowitz, who also le Big Law, has a blog-casting platform, ReplyAll. ey discuss the boredom, even resentment, they say newer lawyers express. Abramowitz categorizes the scenarios that cause errors in human-conducted due diligence: "Timing — lots of people have

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