The US legal system has come in for a lot of criticism of late, with Netflix’s hit documentary Making A Murderer shining a light on a perceived injustice that many have taken to be representative of endemic flaws. In fairness, it’s a big system. US courts see 350,000 cases pass through each year, and when the ABF last recorded the number of registered American law firms back in 2000, it counted 47,563 - a number that has surely risen since.
All of these cases create a wealth of data and information. Research has always been central to the attorney’s role, and combing through the large volumes of informations that pertains to a case is incredibly time consuming. It’s also incredibly costly, with lawyers and law firms spending around $8.4 billion on it annually in the U.S. The quicker and more accurately an attorney can find information that’s actually useful, the more time that can be spent on developing an effective litigation strategy.
Machine learning algorithms are capable of building computer models that make sense of complex phenomena by detecting patterns and inferring rules from data. They have already proven themselves an excellent tool for speeding up processes across a number of industries, as well as discovering important details that humans may overlook. The legal profession should, theoretically, be no different, and it could provide law firms with a cheaper, and better, alternative.
This is made all the easier because an increasingly large amount of case information is now digitally stored. This was highlighted at the Big Law Business Summit in New York City by federal district court judge Shira A. Scheindlin (S.D. N.Y.), who discussed the dramatic ways that TAR is changing the legal world. She said: ‘The use of electronically stored information is everywhere. There is no case — civil or criminal — now that does not involve ESI. ‘Evidence’ has been modified from what was once tangible and testimonial to what is now electronically stored. From email to GPS, from social media to the cloud, from body cameras to cellphones—very little happens that is not recorded.’
Currently, the two largest companies in legal data-driven research are LexisNexis and Westlaw. They have databases that contain huge numbers of case details, and often serve as the default starting point for legal researchers. However, they are not a resource for running advanced analytical tools. Others are filling this gap in the market though. Brainspace, for example, is applying more analytically driven tools to unstructured data in ways that companies have previously not been able to do, and one application that it’s been used for successfully is legal files. They used their software to parse millions of unsorted, unstructured emails from the Enron Scandal. While it took reviewers months to go through it during the original trial back in 2000, Brainspace managed the feat in under an hour. Its analysis also helped discover a number of connections that lawyers had missed the first time around, such as what other companies may have been involved, and what time of day and whereabouts suspicious activity had taken place.
Machine learning can also help in other ways. One of the basic cornerstones of the United States common law system is that judges must explain their decisions in writing. They set out the reasons for their decision by referencing the law, facts, public policy, and other considerations upon which the outcome was based. Machine learning can find correlations between this opinion and other factors to determine whether there are any irregularities that impact a decision and test the system’s strength - such as racial factors for example. It can also help lawyers to find which judges could potentially be more sympathetic to their client.
There are a number of problems with using data analytics. For one, the information that law firms are analyzing belongs to their clients, and it needs to be properly anonymized before analysis. There is also some debate as to whether machine learning algorithms are really the best tool for lawyers to use. Many believe that legal practice requires cognitive abilities that are currently beyond the realm of machine learning and AI. According to a paper in the Washington Law Review by Harry Surden, Attorneys must routinely use both abstract reasoning and problem solving skills in environments of legal and factual uncertainty. Modern AI algorithms, by contrast, have been unable to replicate most human intellectual abilities, falling far short in advanced cognitive processes—such as analogical reasoning—that are basic to legal practice.
It could also have tremendous implications for lawyers, particularly in the kind of background and experience that law firms look for. Traditionally, lawyers have come from humanities backgrounds that tend to involve a heavy research aspect, but as researching abilities begin to play a less prominent role, it is likely we will see more lawyers coming from data science and analytical backgrounds, such as finance.
It is doubtful that machine learning will ever fully replace crucial attorney tasks, but they are likely to be an extremely useful tool. Every lawyer has a duty to provide the best possible service to their client, and if they are ignoring valuable tools like machine learning, they are failing in this.