There was a time, not so long ago, that the term Artificial Intelligence was only used in STEM industries and science-fiction.
And while the view of AI as hyper-smart robots taking over the world is beginning to dwindle, there is a prevailing feeling that certain industries are exempt from its influence.
Often, professions that rely heavily on natural language and human analysis are the quickest to reject the merits of AI, citing innate human judgement and intuition as an irreplaceable requirement of the job.
But with Artificial Intelligence already transforming numerous sectors, tech experts are unanimous in their belief that the technology will continue to have a major impact on the day-to-day work of legal professionals and change the legal sector exponentially.
Earlier this year, legal journalist Madeline Dunn predicted that AI would be one of the key instigators of change for UK law firms in the next five years.
In an article for legal news site legaljournal.com, she said: “UK law firms are beginning to embrace evolving technologies to increase efficiency. Whether it’s using predictive coding software for a more rapid e-disclosure process, tools such as Lex Machina for legal research or, Uhura AI for document assembly, AI will play a big part in the future of law.
“It will also be involved in the development and roll-out of online dispute resolution (ODR). Back in 2019, for example, iCan Systems became the first company to use a “robot mediator” to resolve a dispute. This is set to become a trend for small financial claims. The Law Society recently predicted that by 2038, 67,000 legal jobs will be replaced with automation, revolutionising legal processes.”
From streamlining due diligence to predicting the outcome of court cases, AI is becoming an increasingly common tool. In this article, we will explore a few of the ways the industry is using AI to its advantage.
Verifying facts and figures to thoroughly assess a legal situation is one of the primary responsibilities owed by lawyers to clients. This due diligence is necessary to inform clients rationally about their options and the potential legal actions they could take, all the while being aware of risks.
While intensive due diligence is likely to yield long-term shareholder returns, the process can be time-consuming and laborious, with ample opportunity for human error. Many factors increase the likelihood of mistakes slipping through – late nights, heavy workload, environmental distractions. Last-minute changes to deal structures may also lead solicitors to neglect due diligence.
To combat this issue, an increasing number of law firms are turning to AI for assistance. There are hundreds of programs that can accurately check due diligence contracts by probing, highlighting, and retrieving applicable information for examination.
Some software can be programmed to share information automatically with other team members, to respond to risk management and compliance requirements, and to accurately populate contract management systems with retrieved data.
Tech companies like eBrevia are harnessing the power of AI to streamline document review processes, using “natural language processing” and “machine learning” to retrieve important textual information from contracts and legal documents. The company claims its software can reduce manual review time by up to 90%, as well as eliminating easy-to-miss human errors.
According to AI researcher Helen Bradley, the time saved by using this kind of tool is not only improving law firms’ client relations but also sparking the interest of young and newly qualified solicitors.
Speaking as part of a panel on legal AI, she said: “Law firms have got to be using it these days, not just because clients demand it but also to hire and retain high-quality talent. Junior lawyers don’t want to spend all day and night in data rooms carrying out largely non-legal tasks.
We want our juniors to be doing what junior lawyers should be doing, not administrative tasks like sorting data. So, for the sake of our junior employee’s career development, bringing in AI and other technological tools is the way to go.”
While the world of contract law has welcomed AI with open arms, other sectors are beginning to warm up to its benefits too. US-based tech company Casetext has designed a program that allows lawyers to predict what arguments an opposing counsel is likely to use in the courtroom, by scanning millions of opinions previously used in relevant past cases. Users can also discover cases that have been negatively treated and flagged as unreliable by lawyers.
Predicting legal pitfalls and outcomes has been a popular focus for tech developers in the legal sector, with thousands of companies across the globe turning their attention to improving forecast accuracy.
The demand is growing for software that can detect the early warning signs of litigation, rate individual solicitors likelihood of winning a case, and even predict court outcomes based on the previous leanings of a specific judge.
Software company Ravel Law analyses data and referenced language from almost 500 individual courts, joining the dots between relevant case law and judicial outcomes to predict the most likely conclusion for current cases.
Its dashboard features a wealth of data about past cases, citations, and specific judges, which lawyers can use to guide them as they build a case strategy.
While this might sound a bit “sci-fi”, Ravel Law CEO Daniel Lewis has been quick to dispel any notion that his software could interfere with the judicial process.
Speaking to artificiallawyer.com, he said: “What Ravel can do is help a lawyer to build up a picture via multiple micro-predictions, for example with regard to a particular motion occurring based on past trends, or a specific argument being cited. But, it’s hard to predict the outcome of a big case. For me, the law is not all about art, or all about science, it’s both.”
In terms of accessibility, the jury is also out. Technology like this requires a huge amount of data, with success predictor company Krantz admitting that most law firms simply don’t have big enough databases to support the software.