In part I of this two-part series, I covered the history of computers versus humans playing perfect information board games. In part 2, I talk about what lawyers should take from Lee Sedol’s recent loss to AlphaGo in a five-game Go match. Given the length of the series, I have cross-posted the entire piece on “The Algorithmic Society.”
When Thinking About Automation, Think Tasks Not Jobs
While no one is suggesting that board games are one step away from practicing law, AlphaGo’s significant step forward from Deep Blue and Watson suggests why lawyers must embrace a different future. I call this future the “augmented lawyer.” Lawyers leverage the growing power of computers by using them to handle “power and volume” tasks while lawyers contribute skills computers have not mastered.
Artificial intelligence is not an “either or” situation, often phrased as either the computer can do the lawyer’s job or it can’t. Lawyers’ jobs are composed of thousands of tasks. Some tasks are extremely complex, but many are very simple. Most are mixed. Legal service processes can be disaggregated, for example, during the lean thinking exercise of process mapping. Once a process is disaggregated, the question is whether computers can automate a task or even part of a task. AlphaGo’s win shows there are many tasks lawyer do that computers can learn.
We already have seen computers tackle legal tasks that require significant power or involve lots of data. Consider Shepardizing a case. Shepardizing is a term that dates back to before the modern computer era and comes from a series of books published as Shepard’s Citations (named after the original author, Frank Shepard). Shepard’s books listed all of the published case decisions by federal and state courts. They were arranged by citation. Underneath each case citation heading, Shepard’s listed subsequent cases, that is, cases referencing the case cited in the heading. Shepard’s used symbols to indicate why each subsequent case cited the heading case (overturned, reaffirmed, questioned, cited, etc.).
Lawyers want to know whether the cases they present to judges in briefs are still good law. To Shepardize using books, the lawyer would look up the main citation in the most recent bound Shepard’s volume. Then, she would move to unbound volumes covering each month since the last bound volume. Then, she would move to small booklets covering the weeks since the last monthly volume. She had to repeat this process for each case cited in her brief.
Once she knew all the cases citing a case in her brief, she had to decide which ones to review. If Shepard’s listed five cases citing her case, she might not need to read all five. But if Shepard’s indicated any of the subsequent cases had questioned, limited, or overturned her main case she had to read the subsequent cases. That meant going to the library, tracking down the book with the published case and either reading it right there or photocopying it for later reading. If there was a significant lag between finishing the Shepardizing process and filing the brief, she had to check each case against the latest small booklets. Junior associates had the pleasure of Shepardizing and it took quite a lot of time and client money.
The Shepardizing process is quite different today. Software automatically checks each citation in a brief against databases of published decisions. Within seconds, the lawyer has a list of all cases citing any of the cases in her brief. By clicking on a link, she can go directly to a citing case and the place in the case where the citation occurs. The tasks of finding the citing cases and tracking down the published decisions have been automated, significantly reducing the time spent on those tasks. If there is a lag between Shepardizing and filing, updating means clicking the button again and checking any new cases. Lawyers still must read the citing decisions and draw conclusions about whether those cases have any impact on the case they cited. Large Parts of Legal Service Processes Already Can Be Automated
Most legal service processes involve this combination of tasks amenable to automation and tasks that lawyers still must do. The question is not whether the computer can replace the lawyer, but which tasks the computer can do more quickly, less costly, and with higher quality than the lawyer. The number of tasks where the computer excels is growing, but lawyers resist change and are holding on to those tasks. That resistance increases costs to clients while also reducing quality.
The false “either or” dichotomy masks a continuum. By using process mapping, continuous improvement, and workflow automation reviews, lawyers can construct a pipeline where processes have waste removed and tasks move to automation when the timing and cost make sense. Instead of fancy, complicated software systems lawyers can use simple, low cost and easy to modify automation tools.
A recent article by Michael Chui, James Manyika and Mehdi Miremadi in the Harvard Business Review made estimates about the percentages of daily tasks knowledge workers perform that could be automated. Thirty percent of the tasks performed by knowledge workers can be replace with current automation. Add to that another 20% reduction in tasks from taking waste out of processes (waste reduction estimates range from 20% to 95%, so using 20% is conservative). That means lawyers should be able to get a 50% reduction in the tasks done by attorneys be eliminating or automating them without significant spending or overwhelming time investment.
AlphaGo’s Victory May Be A Tipping Point
AlphaGo’s victory does not mean a defeat for lawyers. But in three ways it is a warning.
First, given that only a year ago experts were predicting it would take a computer another decade to beat a human at Go, it shows the pace of machine learning development is faster than anticipated. The more lawyers resist automation, the farther behind they fall. The faster computers move up the machine learning curve, the greater the gap between what computers can do and how lawyers use them. This will accentuate the difficulty of becoming an augmented lawyer.
Second, lawyers base many of their arguments against using computers in legal services delivery on the distinction between rote tasks (find all documents with the word “discriminate”) and legal thinking. AlphaGo’s use of something akin to intuition shows that, at a minimum, computers are capable of more in law than their critics had claimed. In truth, this already was the case but the AlphaGo win makes it more apparent.
Third, lawyers argue that the cost of training a computer system outweighs the benefits received from the trained system when it comes to the legal tasks that system could do. Hassabis’ statement that AlphaGo will be able to train itself how to play Go and defeat a human player in less than a year shows that the upfront burden of bringing computers into legal services will soon be, or already is, dropping.
Today, anyone with a Macbook, a basic knowledge of Python programming, and access to a data set also can turn loose machine learning on a problem. Many machine learning programs are readily available. Google is even making some of its work available (see TensorFlow, an open source Machine Learning system, now expanded by Cloud Machine Learning “a framework for building and training custom models to be used in intelligent applications”). While good data sets are hard to come by in the law, they are becoming more accessible every day. Soon, we will have all reported U.S. caselaw available to scholars and not long afterward, to everyone. With greater dissemination of the tools and more access to data sets, the power of computers to automate legal tasks will grow and at a faster rate than it has grown before.
No one should minimize the difficulty of jumping from what AlphaGo did to understanding and manipulating language and higher level legal thinking. But assuming some barrier exists is foolhardy. The question is not if, but when. Lawyers must take heed and fully engage in understanding how to work with computers more effectively as part of an augmented lawyer practice. It’s time to go.