AI For Lawyers
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AI For Lawyers

How Artificial Intelligence is Adding Value, Amplifying Expertise, and Transforming Careers

Noah Waisberg, Alexander Hudek

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eBook - ePub

AI For Lawyers

How Artificial Intelligence is Adding Value, Amplifying Expertise, and Transforming Careers

Noah Waisberg, Alexander Hudek

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About This Book

Discover how artificial intelligence can improve how your organization practices law with this compelling resource from the creators of one of the world's leading legal AI platforms.

AI for Lawyers: How Artificial Intelligence is Adding Value, Amplifying Expertise, and Transforming Careers explains how artificial intelligence can be used to revolutionize your organization's operations.Noah Waisberg and Dr. Alexander Hudek, a lawyer and a computer science Ph.D. who leadprominentlegal AI business Kira Systems, have written an approachable and insightful book that will help you transform how your firm functions.

AI for Lawyers explains how artificial intelligence can help your law firm:

  • Win more business and find more clients
  • Better meet and exceed client expectations
  • Find hidden efficiencies
  • Better manage and eliminate risk
  • Increase associate and partner engagement

Whether focusing on small or biglaw, AI for Lawyers is perfect for any lawyer who either feels uneasy about how AI might change lawor is looking to capitalize on the evolving practice. With contributions from experts in the fields of e-Discovery, legal research, expert systems, and litigation analytics, it also belongson the bookshelf of anyone who's interested in the intersection of law and technology.

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Information

Publisher
Wiley
Year
2021
ISBN
9781119723882
Edition
1

PART I
The Point: AI in law is here to stay. It's time to take advantage

CHAPTER 1
How Lawyers Learned to Stop Worrying and Love AI

Simon G. is a 46-year-old corporate partner in a major New York–based law firm. He had been a partner for nearly 10 years when he took over as the relationship lead with one of the firm's top clients, a prominent Fortune 500 corporation.
This client was a major source of revenue for Simon's firm and several others. For many years, the firm was on the client's “panel” of legal service providers. To do any legal work for this company, you had to be on its panel. Each firm on the panel was designated for specific types of engagements and projects, and each would form its own deals with the client.
Everyone at the firm who worked on this client's “team” knew in-house lawyers and executives there very well. They had longstanding bonds formed over weeks-upon-weeks cooped up in conference rooms working on deals, as well as dinners, drinks, Yankee games, theater nights, parties, and more. The families of the partners and those of the corporate executives also got to know each other and would be invited to weddings and other family events. One senior partner at the firm even bought a summer house to be near a bunch of executives from this client.
Every three years, the client would go through the process of reselecting its panel of law firms to represent the firm. During each selection process over the decade in which Simon had been a corporate partner, the process had proceeded seamlessly, without even a hiccup.
Now, several of the firm's senior partners were beginning to transition into retirement. Simon was in a position to take on the leadership role of this major client relationship. This was everything he had worked toward. But, as he prepared to take over the leadership role, he quickly found himself in a major predicament.
This time, something was very different in the panel selection process. Instead of Simon's firm and other top-tier firms offering their typical 10–20% discounts, several top-notch firms, including a few that had never served on the panel before, were offering crazy discounts, some as much as 50% below their normal rates. Simon knew that these were excellent firms; he couldn't knock their quality, and he couldn't understand how they could afford to offer such low rates. Worse, he knew his firm could not afford to compete against these offers. Simon's heart sank. He realized that despite decades of great work and strong relationship development by Simon and his mentors, it was painfully clear that the firm was going to be priced out of working with this important client.
Shocked by how the panel selection was going, Simon immediately got on his computer and started doing what he should have done years prior to the panel review—discovering how law practice was changing, rather than assuming the longstanding relationship with this client would simply continue uninterrupted.
Simon spent hours over the next several days studying the competitive landscape, learning about what he and the retiring senior partners had missed. They had overlooked a very important aspect of today's legal industry: the greater drive for efficient work. Now Simon would have to figure out how to make up for falling so far behind his competitors. What he learned was that his competitors, thanks to innovations like AI, were able to do better work in less time. Through tracking and analyzing the time spent to do tasks as well as realization rates, Simon's competitors could figure out how to offer lower unit prices and still make money. Simon's firm was plenty sophisticated when it came to their legal skills, but, Simon was coming to realize, they were seriously outgunned when it came to the modern practice of law. To remain competitive, Simon and his firm would have to embrace technology in a big way to win over major clients and potentially impress their (now former) biggest client in three years at the next panel review.
Simon's problem was not uncommon, and not unique to Biglaw.
If you're a solo estate planning lawyer, how do you compete with online legal solutions like LegalZoom, who offer a will for $179?
If you're a small firm litigator, how do you compete with a bigger firm that has access to case data that's not as easy for you to obtain?
If you have a high-volume practice, how do you compete with firms that spend less time on customer intake because they use software that shortens the intake process and provides clients with self-help?
Now the question for Simon and his law firm was, could they do it? Could they get back in good favor with their most prestigious client?
AI has been a godsend for countless young law firm associates who once toiled late into the night to gather and review data, but has it played a more significant role across law practice? Let's find out. Before launching into the pros and cons of AI and the resistance and opportunities we have encountered, let's explain our definition of AI.

What Is AI?

For the purposes of this book, we consider AI to be any task a computer does that shows “human-like” intelligence or better. The precise edges of this definition are less important to us than the overall impact that AI and similar technologies have on society and the practice of law. To illustrate, let's talk about a few prominent types of AI tasks and techniques.
The field of AI encompasses many subdisciplines, including machine learning, expert systems, and other reasoning technology. At different points in history, a particular technique might be the face of AI. Although expert systems were once all the rage, today deep learning (a type of machine learning) is extremely popular.
In fact, not too long ago, arithmetic was considered an intelligent activity that only humans could perform. The term computer originally referred to people who did arithmetic and other math, not a machine that runs software (see Figure 1.1).
We wouldn't consider arithmetic to be artificial intelligence today, but 70 years ago, seeing a machine do this was magic. This shows how the definition of AI has a tendency to change over time. As tasks that we once considered untouchable by computers become routine, our definition of “human-like” intelligence becomes narrower. It's no longer news that computers can dominate at games of chess, and many people today take it for granted that they can speak to their phones. Self-driving cars exist and might become equally ordinary in the years to come.
Photo depicts the early computers at work: Dryden Flight Research Center Facilities.
FIGURE 1.1 Early “computers” at work: Dryden Flight Research Center Facilities.
Source: From the Dryden Flight Research Center Photo Collection
AI can replicate certain aspects of human intelligence, such as pattern matching or categorization, and can often do such tasks much faster and more accurately than humans. However, AI doesn't have motivation and emotion like a human, and is generally not able to do things it wasn't designed to. The notion of a rogue AI is pervasive in popular culture and movies, but the reality is much less frightening. The AI that can learn languages is different from the AI that can hit a tennis ball, and there is no general connection between abilities. You can't assume that just because AI can win at Jeopardy, it will, therefore, make an amazing courtroom advocate. Those are different things. Doing one thing well doesn't mean it can do the other. Although we tend to promote the idea of AI having human intelligence by giving it human names such as Siri, Alexa, or Hal, it's still unable to emulate most of the human thought process, for better and for worse.
All that said, AI is able to do many remarkable things, such as understanding human speech, articulating responses, even writing passable text! How does it do this? It uses expert systems, machine learning, and constantly emerging innovation.
First let's talk about expert systems. These are computer systems that emulate the decision-making process of a human expert by asking a cascading series of questions. For example, an expert system might mimic what your doctor would do when they're making a diagnosis. It may ask: Do you have a fever? Do you have headaches? Do you feel dizziness? And so forth, then propose a diagnosis based on the answers you provided. The questions and decision trees in these systems must be handcrafted by human experts, generally falling into the “rule based” or “reasoning” subfield of AI. Expert systems are a good tool for a variety of tasks, but in many areas they are being replaced by machine learning.
Most of the AI you see in the news today is based on machine learning, including all the various deep-learning advances. Machine learning techniques allow computers to learn to perform tasks simply by observing data provided to them. It doesn't need experts to manually write complex rules, though it still does need to observe people to learn from them. Although the origins of machine learning are as old as those of expert systems, machine learning techniques didn't become widely effective until computers became more powerful. These systems excel at modeling unpredictable and complex tasks and can learn at a rate and scale far beyond what humans manually encoding knowledge in rules could achieve.
From driving a car, to serving as personal assistants, to face recognition, to web translation, to recommending a comedy you might like on Netflix, various types of AI are part of our world in big and small ways. In this book, the technology we discuss falls under our definition of AI. Others may have slightly different definitions of what “AI” is, but we would rather talk about its impact in law practice than debate the exact boundaries of the terms.
In the legal world, AI is being used for contract drafting, negotiation, and review; litigation document review and analysis; predicting case outcomes; suggesting courses of action; organizing legal research; time keeping; and lots more. It is opening up possibilities never before imagined and allowing lawyers to spend more time on law and less time on repetitive activities. AI is partner...

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