Lexpert Magazine

54 LEXPERT MAGAZINE | SEPTEMBER 2017 | ARTIFICIAL INTELLIGENCE | could be automated, the financial impact on the industry is an open question." 11 What is AI good at? AI is useful in M&A due diligence for that which (1) is routine, (2) voluminous, (3) needs custom- ization, or (4) is nearly invisible and pos- sibly fraudulent. As Waisberg said, "Since lawyers were looking for the same provisions so fre- quently (e.g., change of control, assign- ment, exclusivity), I thought it might be possible to build soware to help find this info." 12 is is the "quick win" of AI: it can simply take in more volume on rou- tine functions than a human can. It is the voluminous work of reviewing hundreds or, indeed, thousands of documents of the same type that holds out the most accepted potential for AI. At the next level, AI is highly susceptible to customization. For example, take Brexit: "Brexit Contract Review Solution is a new collaboration between NextLaw Labs (Dentons law firm's legal tech investment vehicle) and RAVN Systems. e solution lever ages RAVN's AI technology and a be- spoke algorithm co-developed with Den- tons' subject matter experts to enable high- volume contract review to pinpoint provi- sions that the UK secession may impact." 13 In discussing the development of their company NexLP, which uses AI to ana- lyze data and identify trends, co-founder Jay Leib describes the questions he and his partner, Dan Roth, started to consider in the late 1990s: "What is the future of the industry? We thought about it," he says. "ere were whistleblowers in their companies who knew what was going on, and the unstruc- tured data contained the stories. Compa- nies could detect potential problems early on, provide alternatives to counsel and the C-suite, and understand their exposure. It would prevent unnecessary legal spend and mitigate risk, thus protecting the compa- ny's brand and shareholder value." 14 "Nearly 80 percent of a company's data is unstructured," Leib says. "While unstruc- tured data represents the lion's share of a company's data, for years lawyers have been stuck with antiquated tools that focus pri- marily or solely on Boolean search. Better tools are needed to truly understand data, infer meaning, classify the various types of ideas present, and help you get to the result diligence? Waisberg replies: "e junior lawyers who do most of the review don't actually know what they're looking for. As smart and well-educated as they may be, and even though their firms may have fancy training programs, it takes practice to spot change of control clauses/exclusiv- ity/non-compete/MFN. is is the system- atic component of how mistakes get made in due diligence. e random component of errors is that diligence is oen done by tired, distracted, rushed lawyers. Surpris- ingly, doing diligence at 2 am night aer night does not improve quality." Many associates are in a certain nega- tive mood about the efficacy of manual due diligence. Lawyers, being human, get tired and cranky, with unfortunate implications for voluminous due diligence in M&A. Slaughter & May's Steve Cooke says AI in M&A due diligence "will give junior law- yers … their lives back." But what will lawyers do with that new- found time? A Forbes magazine testimoni- al on the Ross website has it that "… lawyers will be able to avoid some of the mundane tasks…" 10 "By automating labor-intensive, low-value tasks, artificial intelligence systems free up lawyers and other legal professionals to concentrate on complex, high-value projects. But when between 13 and 23 percent of the average lawyer's time fast — even if that result didn't involve the keywords you used." 15 Document reviewers have always tried to detect what a target company might be concealing, but now vendors of AI tools are developing "technology that can turn information into stories. Story Engine is a program that can read through unstruc- tured data and summarize conversations, including the ideas discussed, the frequen- cy of the communications and the mood of the speakers." 16 is is not to say that computers can feel the mood of speakers as humans to varying degrees can, but computers can ingest more unstructured data than we can and, within that, recognize patterns. en enter the humans: corporate leaders will ultimately still make the decision to pursue deals are not. And they will continue to be greatly assisted by internal and external counsel when it comes to strategy. It takes wisdom to discern, "What does it all mean?" So is this what humans do better? Dis- cern meaning in a deal, using judgment? In Algorithms to Live By: e Computer Science of Human Decisions, Brian Chris- tian and Tom Griffiths explore the ways in which humans can combine "computer al- gorithms" with human qualities in order to make decisions. ey offer the o-told an- ecdote about Charles Darwin composing a "pro and con" list to answer the question, for himself, as to whether or not he should marry his cousin Emma Wedgwood. Based on a "narrow margin of victory," Darwin concluded, "Marry … Q.E.D." 17 Christian and Griffiths continue, ex- plaining that it was Benjamin Franklin, before Darwin, who devised and praised "Moral or Prudential Algebra," in which the more factors considered the better. 18 Not so in the present age, according to Christian and Griffiths: "e question of how hard to think, and how many factors to consider, is at the heart of a knotty prob- lem that statisticians and machine-learning researchers call 'overfitting.' And dealing with that problem reveals that there's a wisdom to deliberately thinking less. Being aware of overfitting changes how we should approach the market …" 19 But Darwin proved his decision, didn't he? And in this paper, we have been prais- ing all this data that computers can process for the benefit of M&A. Why are we now NATALIE MUNROE OSLER, HOSKIN & HARCOURT LLP "Knowledge of the software and how to apply it is key. … The software augments the legal expertise, but at this stage, it cannot replace the legal judgment that must be applied to complex issues."

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