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Big Data Meets Big Law

Will algorithms be able to predict trial outcomes?

By Tam Harbert Contact All Articles 

Law Technology News

December 27, 2012

Daniel Katz
Photographer: Scott Stewart

Editor's note: As 2013 approaches, what better time to look back at the last year to see what topics and trends dominated legal technology, and predict what we will still be discussing next December. Retrospection helps analysis and planning, so for the last few days of the year, LTN will be reprising its top stories from 2012. In the story "Big Data Meets Big Law" from the May/June issue of LTN, reporter Tam Harbert puts a future tense in the answer to a common question posed to counsel: "Can you win this case?"

"What are the odds of winning this case, and what's it going to cost me?" Those are questions clients routinely ask their attorneys. Today, lawyers draw on experience and gut instincts for the answers. Sometimes, they are even right. It may not be long, however, before computers spit out answers with far more accuracy.

Legal scholars, computer science engineers, and commercial companies are building databases and using algorithms to crunch massive amounts of historical legal data to identify the significant factors that influence particular legal outcomes.

These experts say that such factors can then be used to predict what will happen in future scenarios. Called quantitative legal prediction, it's basically what happens when the latest technology trend — called "big data" — meets the law. And it just might change how corporate general counsel and BigLaw manage legal matters and costs, how they craft legal arguments, and whether, how, and where they file a lawsuit.

The trick, however, is getting usable data. So far, finding comprehensive legal data in a form that computers can handle has proven difficult. Unless that problem is solved, the technology may have a more limited impact. Already, though, quantitative legal prediction has started "coming in at the edges of tasks that lawyers do," says Daniel Katz, assistant professor at Michigan State University College of Law. E-discovery, for example, uses algorithms to review reams of documents and predict which are likely to be relevant in a given case.

But that's just the tip of the iceberg, says Katz. There's been a quiet transition in the legal industry that most people are largely unaware of, he says. Reasoning traditionally done by human attorneys can be replaced or supplemented by predictions made by computers. "It's not going to end lawyering ... but I definitely think some percentage of tasks that lawyers do are going to be replaced by machines and/or technology," says Katz.

One company that is trying to capitalize on the potential of this technology is TyMetrix, part of Wolters Kluwer Corporate Legal Services. A vendor of e-billing and matter management systems for corporate law departments, it started collecting data on billings and legal matters in 2009. With its customers' permission, it has accumulated data from $25 billion in legal spending, which is stored in a data warehouse. TyMetrix is using analytics to mine the information for use in products, says Craig Raeburn Jr., managing director of TyMetrix Legal Analytics. One product that benefits from the analysis is the $2,500 Real Rate Report that benchmarks law firm rates and identifies the factors that drive them.

TyMetrix also offers a free app for mobile devices that uses Real Rate Report data to serve up average hourly legal rates of law firms across the country. The company's goal is to help customers manage future legal costs, says Raeburn, who notes that TyMetrix plans to integrate a rate analysis and forecasting feature into several products. For example, the technology could identify and analyze five to 10 key variables in a certain type of legal matter and then predict costs for a future case. "You can see the entire case costs and you can play what-if scenarios," he continues, to figure out the most cost-effective way to manage a matter. If you spend less on outside counsel, for example, how might that impact the outcome of the matter?

Of course, such predictions are only as good as the size and quality of the data. TyMetrix's data warehouse is limited — the company has gathered only certain types of data and only from clients who've opted in. But it is on the hunt for additional data sources, says Raeburn, although he is cagey about exactly where that data may come from. "There are other sources that are available in the market where people can now get more information than they have ever been able to," he says.

Katz concurs that harvesting data can be a challenge. "The problem is you have to collect massive amounts of data, and a lot of it is not easy to get," he says. The most obvious data source, the Pacer (public access to court electronic records) system, is notoriously difficult to access. And, he notes, "Pacer is not free. You have to pay for it."

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