Imagine you've been served with a legal complaint. Your startup company makes a very popular widget, and your chief rival, MicroWidget International, is suing for patent infringement. If MicroWidget prevails, you could be out tens of millions of dollars.
You go to your in-house lawyer, who recommends that your company hire a patent expert at Moneybags & Moneybags, LLP. The next day, a brigade of Moneybags lawyers marches into your firm and outlines your options. Defending the MicroWidget case will be costly—in addition to the thousands of billable hours for the attorneys assigned to your case, you'll have to hire expert witnesses and jury-selection specialists, and pay for travel and court filing fees. The total legal bill will be about $5 million, give or take. But Mr. Moneybags, Esq., the firm's ancient senior partner, assures you it will be money well spent—he's worked on many of these cases, he says with a wink, and he's got a gut feeling you'll win this one.
Your other option is to settle with MicroWidget. You could pay a licensing fee of $10 million, which would be painful, but not fatal, to your firm—and it would allow you to go on with your business.
What do you do?
There's no easy answer. The legal industry is one of the few remaining outposts of the corporate world whose operations are dictated mainly by human experience. Basic questions that anyone would want to know before committing to a million-dollar case—How likely is it that I'll win? How good are my lawyers? Should I settle?—can't be answered with certainty. "There's a culture in the law around expertise," says Daniel Katz, an assistant professor at the Michigan State University College of Law who is among the vanguard of legal researchers working to bring empiricism and artificial intelligence into law. "There's a lot of human intuition, and people tend to think that whatever legal knowledge they have is uniquely human, and not subjectable to data and computers and automation."
Katz is working on something he calls "quantitative legal prediction." Thousands of patent cases are filed every year in the United States. There's a good chance, then, that MicroWidget's case against you shares some similarities with a bunch of those other cases. What if you could analyze the key features of MicroWidget's claim, and then see how thousands of comparable cases fared? "Lawyers will be able to say to their clients, 'Here's what we think your chances are—and based on 10,000 cases that are just like yours, here's what the computer thinks your chances are,' " Katz explains.
There is no machine that does this today, but it's coming. In the last piece, I examined the technology that's encroaching on journalism—computers are getting so skillful with language that they can now write stories all by themselves. But journalists are small fry; if you want to go after a profession that relies heavily on language, the deep-pocketed legal world is a fatter target.
In the last few years, the law has seen a rush of technological innovation, all stemming from computers' increasing capacity to decipher and understand written documents. Many law firms now use "e-discovery" tools that can scan large caches of evidence in search of interesting facts and figures. Firms also have software to draft legal documents in a fraction of the time a human would take. And a few services on the horizon might do even more—negotiate the terms of a contract, for instance, or determine whether or not you should sue.
Automation will bring legal services to the masses. Many people who ought to hire an attorney to handle business or personal disputes can't afford to do so. Software could potentially step in when you want to fight your mortgage lender, draw up contracts to start a small business, or sue for child-support payments.
While legal automation will be a boon for those who can't afford representation, it's bad news for lawyers. The industry is already in a slump, and law school is no longer seen as a sure path to riches. Because software will allow fewer lawyers to do a lot more work, it's sure to drive down both price and demand.