law practice management tips & tricks
Artificial Intelligence in the Judiciary On October 7, 2001, Dodgers pitcher Dennis Springer threw a 43 mph knuckleball in the bottom of the first inning to the San Francisco Giants’ Barry Bonds. Bonds connected with a crack and the ball flew into the left-center stands for a record-setting 73rd home run. Alex Popov was in the stands at Pacific Bell Park ready and waiting with his glove when Bonds’ 73rd home run ball came his way. For a second, the ball was in his mitt before he was taken to the ground by a wild mob. The ball rolled away from Popov in the scrum and another fan, Patrick Hayashi, eventually emerged with the prized ball in hand. Popov looked on with dismay. (Video of the melee is available on YouTube at http://bit.ly/popovvhayashi.) Popov urged Hayashi to surrender the ball but Hayashi refused. Though Hayashi had not committed any wrong-doing in getting the ball, Popov maintained it had become his when it touched his glove and Hayashi had a duty to return it. Eventually, the two would land in court fighting it out in Popov v. Hayashi, (WL 31833731 Ca. Sup. Ct. 2002).
Making a Hard Call
Katie Atkinson of the University of Liverpool’s Department of Computer Science saw in Popov v. Hayashi an exciting opportunity to explore modeling legal arguments as computer code. Could she reproduce a human judge’s ruling by feeding case law into the computer and weighing the arguments of the parties? Atkinson explains her approach in a BBC Radio 4 Law in Action podcast, “You represent arguments as a graph…and then you do a calculation on which arguments attack one another, which are counter-arguments, and then you have to have a method of deciding which are the winning arguments. This argument beats this argument because of this particular reason.” The computer churned the numbers and returned a fencesitting finding. On the one hand, Mr. Popov made a valid claim and it would be reasonable for him to be victorious but Mr. Hayashi made a compelling argument as well and could be an acceptable victor. Neither side hit a legal home run. The computer’s wishy-washy result feels like a cop-out but it mirrored the California Supreme Court’s reasoning. The human court held that both Popov and Hayashi had legal rights to the ball and neither could be lawfully deprived of it so the most equitable solution was the Solomonic order to have the ball sold with the proceeds of the sale divided evenly. Popov v. Hayashi was such an unusual case with facts and arguments tilting either way that the computer model reaching the same result as the humans turns out to be a significant step forward in legal-minded artificial intelligence. Atkinson has continued her efforts incorporating bodies of case law into a computer model which are then fed the facts and arguments of real cases. Her computer has accurately matched the outcome of the human judges in 96 percent of the cases. It should be noted that this is still a relatively small sample of 32 cases, but that is enough to send reverberations through the legal community. Might judges (and lawyers) be replaceable with artificial intelligence?
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The Journal of the Kansas Bar Association
Hidden Agents
Atkinson’s experiments are at a bleeding edge of legal-minded artificial intelligence handling truly difficult tests like Popov v. Hayashi. Other artificial intelligence agents are tackling simpler cases wholesale and have been doing quietly for years. Companies like eBay and PayPal inevitably generate a lot of disputes—mistaken charges, misrepresented items, shipping errors, etc.—so they developed an online dispute resolution tool that uses a form of artificial intelligence to resolve over 60 million cases per year. These “small claims” disputes are simple compared to Popov, but they take time and money to resolve and leave a bad taste in consumers’ mouths if handled carelessly. The artificial intelligence agents can adjudicate these cases in minutes with a fairness that has actually boosted customer satisfaction. Just as courts can raise revenue from filing fees, the artificial intelligence agents can make money resolving cases also, and the online dispute giant, Modria, was spun off from the eBay solution as a separate company offering adjudication solutions for the small disputes that trouble consumers and companies. The market for artificial intelligence adjudication is expanding rapidly with direct offerings to customers through sites like OneDayDecisions.com. Where a small claims case might cost about $50 to adjudicate with several weeks or more of waiting, OneDayDecisions can adjudicate a claim with a form of artificial intelligence for $19 in a day with payment to the prevailing party within 7-10 days.
Black-Robed Robots
It may be some time before the black robes in the courtroom are worn by robots, but we already live in a world where our human judges hear fewer simple disputes due to the efforts of artificial intelligence agents. More likely—in the beginning, at least—artificial agents will be used as assistants which wade through mounds of complex data and help summarize existing or developing case law. In fact, one such artificial intelligence lawyer has already been hired by the law firm Baker & Hostetler. His name is Ross, a descendent of the Jeopardywinning Watson from IBM. Perhaps Ross will one day put his name in for a judgeship after a few more years of experience.n About the Author Larry N. Zimmerman is a partner at Zimmerman & Zimmerman P.A. in Topeka and former adjunct professor teaching law and technology at Washburn University School of Law. He is one of the founding members of the KBA Law Practice Management Committee. kslpm@larryzimmerman.com