Think the lawyers are safe from the robots? Think again
by David Atkins
As mechanization and outsourcing continue to take their toll on the economy, students and those in career transitions have been forced to scramble to careers requiring increasing amounts of higher education to escape the undertow. For many, this has meant going back to grad school or learning to code. For others, it has meant law school.
Because no one can outsource a lawyer, and a robot can't become a lawyer, right? Right?
But the plight of legal education and of the attorney workplace is also a harbinger of a looming transformation in the legal profession. Law is, in effect, an information technology—a code that regulates social life. And as the machinery of information technology grows exponentially in power, the legal profession faces a great disruption not unlike that already experienced by journalism, which has seen employment drop by about a third and the market value of newspapers devastated. The effects on law will take longer to play themselves out, but they will likely be even greater because of the central role that lawyers play in public life.
The growing role of machine intelligence will create new competition in the legal profession and reduce the incomes of many lawyers. The job category that the Bureau of Labor Statistics calls “other legal services”—which includes the use of technology to help perform legal tasks—has already been surging, over 7 percent per year from 1999 to 2010. As a consequence, the law-school crisis will deepen, forcing some schools to close and others to reduce tuitions. While lawyers and law professors may mourn the loss of more lucrative professional opportunities, consumers of modest means will enjoy access to previously cost-prohibitive services.
A decline in the clout of law schools and lawyers could have potentially broader political effects.
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Five key areas of law now face encroachment by this machine intelligence. Some invasions are imminent, and others more distant but no less likely. The area ripest for computational transformation is discovery. As a young lawyer, I spent lots of time rifling through documents to determine which were relevant to an opponent’s request for information. That was the tedious, if lucrative, lot of the junior litigation associate and an important profit center for the litigation group at a big firm. These days, “predictive coding” is removing that labor-intensive task from lawyers. In predictive coding, a small number of lawyers can swiftly sample a large set of documents and construct algorithms—with the help of computer technicians—to decide which documents are relevant. Computers can sort better than people because fatigue, boredom, and distraction reduce human accuracy, while machine intelligence works nonstop, with no lag in attention or need for caffeine or sleep.
“E-discovery” has already become the hottest new phenomenon in litigation. Job growth in this legal area, unlike most others, is strong. One graduate of Northwestern Law School now specializes in head-hunting for professionals who can strengthen law firms’ e-discovery capabilities. And courts now recognize that e-discovery can curb litigation costs and make justice more affordable. For instance, the Federal Circuit Court of Appeals, which specializes in patent cases, has issued a standing order that encourages the use of e-discovery.
Private firms are also beginning to specialize in these sophisticated services. With a combination of computational and legal knowledge, they can innovate more readily than lawyers who are left to their own devices. Last year, Modus raised $10 million to continue its data-driven competition with law firms in e-discovery. Such innovation will render e-discovery more accurate and less expensive, making use of such methods routine.
More than 100 years ago, a jurist wrote: “Every practitioner knows that when a hard case arises, the law books are ransacked from the time of the Norman Conquest and the court blindly applies any absolute precedent that may have been found by diligent counsel.” Even if he exaggerated, searching for the right cases for precedents remains an important legal skill. Yet just as computers have largely replaced humans in making complex calculations, so machine intelligence will supplant lawyers’ legal search function—a second key area to be disrupted.
Until now, computerized legal search has depended on typing in the right specific keywords. If I searched for “boat,” for instance, I couldn’t bring up cases concerning ships, despite their semantic equivalence. If I searched for “assumption of risk,” I wouldn’t find cases that may have employed the same concept without using the same words. IBM’s Watson suggests that such limitations will eventually disappear. Just as Watson deployed pattern recognition to capture concepts rather than mere words, so machine intelligence will exploit pattern recognition to search for semantic meanings and legal concepts. Computers will also use network analysis to assess the strength of precedent by considering the degree to which other cases and briefs rely on certain decisions. Some search engines, such as Ravel Law, already graphically display how much a particular precedent affected the subsequent course of law. As search progresses, then, machine intelligence not only will identify precedents; it will also guide a lawyer’s judgment about where, when, and how to cite them.
Search is also becoming ever more affordable, even as its efficiency increases. Lexis and Westlaw still charge for their superior legal search engines, but free search is now available from FindLaw and Google Scholar, among others, and these sites offer more than adequate assistance for many purposes. Such cost reduction exemplifies the Silicon Valley slogan that information “wants to be free.” Lawyers have traditionally enjoyed leverage over the laity, partly because of their superior access to information. Low-cost legal knowledge poses a threat to that power.
This sort of thing is happening everywhere. Google has decimated the market for political writing as websites struggle for revenue and political organizations have turned to an email-and-petition based fundraising model to survive. The marketing research industry is in turmoil as big data crushes out many traditional research methods. Retail has long been suffering. Education is on the block.
And as artificial intelligence increases, most of those professional jobs everyone is scrambling toward will be gone, too. Or, if not gone completely, they will be glorified button-pushing jobs that pay 1/5 what they used to, which is the other major development in the economy. It's not so much that unemployment is rapidly increasing, but that there has been rapid destruction of industries and wages. The thing to watch in the broader economy is underemployment more than unemployment, as armies of trained professionals cobble together multiple income sources at far less pay.
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