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In 2016, DeepMind’s AlphaGo famously defeated Lee Sedol, an international Go champion, becoming the first computer program to beat a human world champion. In 2018, LawGeex, an AI contract review platform, pulled the same stunt on human lawyers.

The AI system achieved a 94 percent accuracy rate at surfacing risks in non-disclosure agreements (NDAs). Experienced human lawyers average out at 85 percent accuracy for the same task. The study, conducted in collaboration with Duke and Stanford Law Schools, pitted AI against 20 top U.S.-trained lawyers with decades of experience specifically in reviewing NDAs, one of the most common agreements in law. The legal AI system took 26 seconds to complete the review. Human lawyers took an average of over of 92 minutes.

The lawyers who took on the AI had an interesting reaction upon defeat. “It is crucial to make mundane contract work more efficient, especially when there are 50-100 pages of contracts for some major deals,” emphasized Zakir Mir, a counsel for Allegiance International. “AI can really help lawyers sift through these documents, and cut down on the sometimes-deliberate verbosity of these documents which can allow one party to mask core issues.”

The growing legal AI field is seeing startup companies using AI to tackle daily legal work, including drafting and reviewing contracts, mining documents in discovery, answering routine questions, or sifting data to predict outcomes. Legal tech in 2017 saw $233 million invested across 61 deals, edging ahead of 2016 according to an investment report by Tracxn.


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U.S. law firms, for their part, are turning to AI solutions as they experience sluggish growth in demand and decline in productivity. Of the 386 U.S. firms that participated in Altman Weil’s 2017 Law Firms in Transition survey, half reported they have created special projects and experiments to test innovative ideas or methods, and 49 percent indicated they are using technology to replace human resources with the aim of improving efficiencies.

Legal AI is also tackling the complex legal language (“legalese”) of contracts, which has changed very little over the centuries. The National Contract Managers Association‘s annual benchmark survey shows only 12 percent of lawyers think contracts are understandable and easy to read.

Yonatan Aumann, professor at the department of computer science at Bar Ilan University and an advisor to LawGeex, said, “The AI’s mother tongue is actually legalese. Our researchers exposed the AI to tens of thousands of examples of legalese, gradually making its knowledge more refined, training the AI to point out specific concepts in everyday contracts based first on its native knowledge. The algorithm can now identify concepts in such contracts even though each contract is written in a different way.” Meanwhile, other companies are springing up, such as Polisis, a website that uses machine learning to make sense of online privacy policies for consumers.

Legal AI means lawyers are increasingly focused on the present realities and future of the law, rather than just the precedents of the past. Erika Buell, professor and director of the program in law and entrepreneurship at Duke Law, sees a mixed reaction to increased legal automation. “Students shown the software had an initial reaction of ‘this is going to take our jobs.’ Others were excited by it,” she said. The AI’s victory is encouraging new and innovative options to train future generations. Today, many lawyers never make it more than a few years in the profession, with many leaving because they cannot stand the drudge work.

After his defeat by AlphaGo, Lee Sedol won every tournament game he played in the two months following the challenge. Developers of legal AI see the lawyers’ defeat driving a similar innovative spirit. Despite beating human lawyers at narrow tasks, experienced lawyers are still required as “trusted advisors” in important deals. But powerful, legal AI is not capable of being or meant to be a standalone tool. The goal is to use AI and humans to ensure that firms are lawyering more accurately — and more consistently — than humans or technology alone.

Marlene Jia is the CEO of Metamaven, a company using AI to grow business and revenues for Fortune 500 companies. She’s also the coauthor of Applied AI Book: A Handbook for Business Leaders

This story originally appeared on Www.topbots.com. Copyright 2018

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