We use artificial intelligence (AI) every day. Or, perhaps more correctly, machines are learning more about us every day — we just know how to use it to our advantage. Netflix recommends movies based on movies we’ve watched and liked before. Spam filters let us ignore thousands of pointless emails. And Amazon’s targeted advertisements — carefully curated to your buying history and demographic information, among other things — make purchases (and repeat purchases) easy.
In e-discovery and other areas of legal operations, that’s true as well. Many forms of legal technology use some degree of AI. Technology Assisted Review (TAR) and predictive coding have become standard tools of the trade, but they are still underutilized among legal teams. Exterro’s 2019 In-House Benchmarking Report found only 9% of respondents thought TAR helped save time and costs — shocking, considering review is the most expensive and time-consuming e-discovery stage. Another survey by the Corporate Legal Operations Consortium (CLOC) found that only 12% of teams used AI technology.
The motivation of investing in AI should be that it can execute legal tasks faster, more accurately and more efficiently than humans. Deep learning and predictive capabilities that can decrease the time and costs associated with tasks such as identifying responsive data among large troves are improving at a fantastic rate.
So how can attorneys and in-house legal teams begin to embrace AI use? The answer: Seek AI technologies that can help answer current and forward-looking legal and regulatory hurdles to create efficiency in a changing regulatory environment. Namely:
- How do we automate mundane tasks?
- How do we get to the facts of a matter as quickly and defensibly as possible?
- How do we deal with personal data to ensure compliance?
Lawyers as part robot?
For many attorneys and in-house professionals, technology should be lockstep with processes. This requires a deeper understanding of specifically how the technology can save time and money, and therefore why it should be used. The thought of learning a new technology when a current process works just fine can be scary, but ultimately it should help people do their jobs better.
“You’re going to see attorneys using their own [software] more to get things done,” predicts Ronald Hedges, senior counsel for Dentons LLP and former U.S. magistrate judge for the District of New Jersey. “Technology is affecting the profession, but the question is where exactly it’s going to hit.”
Considering that AI can automate many tasks that once required teams of people, it may be expected that headcount is an area most affected by acquiring AI.
“I expect we’re going to see attempts to use technology to reduce staff functions more than anything else,” Hedges added.
The concept of the robot lawyer has been around for some time. In fact, an MIT and University of North Carolina report, “Can Robots Be Lawyers? Computers, Lawyers, and the Practice of Law,” disputed many of the primary arguments that automation and AI would replace the work that attorneys and legal teams now perform. They found that AI is most suited for functions that comprise only a small fraction of what lawyers do. Used properly, AI is best suited to increase productivity and save time, not reduce headcount.
As comfort and awareness of these capabilities increase, AI can help more attorneys and legal teams automate the most mundane and time-consuming tasks to free up resources for more important work.
Predictive coding and new standards
Predictive coding is a machine-learning process that uses software to take keyword searches and logic, entered by humans, to find responsive documents. It typically applies to much larger datasets to reduce the number of irrelevant and nonresponsive documents that need manual review.
But predictive coding doesn’t represent the most forward-looking technology: The ability to smart label during the review stage, for example, leverages the latest advancements in deep learning. By incorporating techniques of active learning, the system is enabled to learn from the reviewers as they evaluate documents and suggests labels for the rest of the non-reviewed documents without the need for dedicated human-machine training sessions.
These may be the latest emerging standards, but David Cohen, head of e-discovery for Reed Smith, says that going forward in this case means going backward a bit: recognizing the value of predictive coding and establishing judicial standards.
“I remember 10 years ago when predictive coding was new in the market, people predicted it was going to replace human review,” says Cohen. “That certainly hasn’t happened, and one of the reasons is because it’s so complicated in terms of what standards apply to it. How are you going to get the other side to accept it? How are you going to get the court to accept it?”
Xavier Rodriguez, U.S. District judge for the Western District of Texas, has a number in mind. “We’re expecting 85% accuracy for a lot of predictive coding,” he says.
Which begs the question: Why isn’t there a standard yet? As AI continues to evolve, and more sophisticated concepts begin to permeate into e-discovery platforms, accuracy and reliability increase. Eventually, they will become commonplace in negotiations.
“It’s not just about the technology, it’s about coming up with standards and making everybody feel comfortable that the AI is accomplishing what it needs to accomplish,” Cohen says. “And, of course in discovery, that means finding the stuff that is relevant, and getting rid of the stuff that isn’t relevant.”
Ajith Samuel co-founded Exterro Inc., with a simple vision that applying the proven concepts of process optimization and data science to how companies respond to litigation would drive more successful outcomes at a lower cost. He leads the company’s product strategy and management team.
This column originally appeared here.
ABA Law Technology Today was launched in 2012 to provide the legal community with practical guidance for the present and sensible strategies for the future. LTT brings together practicing lawyers, technology professionals and practice management experts from a wide range of practice settings and backgrounds. LTT is published by the ABA Legal Technology Resource Center.