The legal services industry is hurtling headlong into a revolution in the way that we carry out virtually every aspect of our jobs. The introduction of artificial intelligence (AI) – intelligence exhibited by machines that are trained to learn and solve problems – is not just an extension of prior technologies. AI holds the potential to dramatically change the field in a variety of ways, from reducing bias in investigations to challenging what evidence is considered admissible.
For corporate legal department teams that are prepared to embrace the power of AI, there is vast potential for increased corporate security, greater productivity in litigation management and improved corporate investigations capabilities.
It’s Already Here
Corporate legal departments, no matter how large or small, can no longer escape the fact that AI capabilities are real. AI is no longer a futuristic concept relegated to science fiction hobbyists. A 2016 survey conducted by the National Business Research Institute (NBRI) found that 38 percent of enterprises are already using AI technologies, and 62 percent will use AI technologies by 2018. “The availability of large volumes of data – plus new algorithms and more computing power – are behind the recent success of deep learning, finally pulling AI out of its long winter,” writes Gil Press, contributor to Forbes.com.
But some people haven’t yet recognized this. Their confusion may stem from their failure to distinguish “Generalized AI” from “Specialized AI.” Generalized AI, which is sometimes known as “Hard AI,” refers to machines that are able to think like humans and perform humanlike reasoning in any area or domain without guidance from humans. Those are ambitious goals that have not yet been attained, which has caused some to falsely conclude that AI is still a project under development.
In reality, Specialized AI – or what is sometimes known as “Soft AI” – is already here. It refers to machines doing work that could only be completed by humans in the past by applying intelligent tools to address a specific task. In this article, we’re going to unpack a few key examples of how AI is helping corporate legal departments better perform their roles in managing risk, litigation exposure and corporate investigations.
How It Helps Legal Departments
“Much has been said about how AI’s machine learning capabilities will transform the legal world,” reported Legaltech News in December 2016, “but only recently has there been evidence of its actual effects. While many corporate counsel struggle to expand their budgets for legal technology, some … are leveraging AI to reach unprecedented levels of efficiency and productivity.”
We have identified three key areas where AI is available to corporate legal departments today: increased cybersecurity, greater productivity in e-discovery and improved corporate internal investigations.
1. Increased Cybersecurity
AI is a tremendous aid in the battle to protect corporate assets in cyberspace. It offers the ability to anticipate, identify and shut down attempted cyberattacks, rather than the purely defensive posture of antiviral patching or the remediation required after an attack has already taken place.
“It can take weeks or months to detect intrusions, during which time attackers can exploit vulnerabilities to compromise systems and extract data,” said Torsten George, writing in SecurityWeek. “To address these challenges, progressive organizations are exploring the use of AI in their day-to-day cyber-risk management operations.”
Current cutting-edge cybersecurity tools train advanced machine learning algorithms to understand the difference between standard and malicious code behavior. Rather than looking for the specific signature of a particular virus or attack, machines now look for characteristics or symptoms of an attack and block the attacks proactively. While this approach is not new, the improvement in accuracy and reduction in "false positives" make these systems commercially viable.
AI not only helps protect against external threats. Increasingly, corporations fear an attack from the inside, from disgruntled employees looking to damage the company's image – or perhaps engage in financial or IP theft. These insider attacks are far more difficult to predict and protect against, since employees must have at least some access to systems in order to conduct their day-to-day work. Smart cybersecurity tools can now monitor system usage logs and alert the company if it detects suspicious behavior.
In the ever-escalating cat-and-mouse game of cybersecurity, AI affords the best protection against an increasingly sophisticated network of criminal and state-sponsored hacking. Corporate legal departments must balance the civil liberties of its employees with the duty of the corporation and its delegates to protect key business assets. AI tools can help by providing a human level of sophistication without encroaching on individual privacy.
2. Greater Productivity in E-Discovery
AI, in the form of technology assisted review (TAR), is key to improving efficiencies throughout the e-discovery process. TAR uses various methods, such as predictive coding, to find and rank relevant documents, saving time on manual review and therefore reducing the amount of money spent on e-discovery. As the machine “learns” what documents in a data set are relevant, the results that it produces become increasingly relevant.
“As the cost to electronically store documents, email messages and other information has dropped, companies have held on to more information, making the search [for documents] more difficult,” according to The Wall Street Journal. “Conventional e-discovery software doesn’t rank search results, which means that lawyers need to manually review information. That manual review process accounts for about 73 percent of the costs in e-discovery. Using software with predictive coding can substantially increase the relevant documents returned.”
Recent advancements move beyond traditional predictive coding to continuous active learning (CAL), which uses newer machine-learning algorithms to continually adjust its understanding of what is important to reviewers and surfaces increasingly relevant content for tagging. This is not only quicker than traditional techniques, it can also handle differences between reviewers and yield more accurate results than older techniques. ROSS, the IBM Watson-powered legal research tool, uses the same approach to train its software. And AccessData's e-discovery software platform is on the forefront of this revolution with advances that help corporate law departments expedite data culling, coding and categorization of large document collections with its statistical TAR engine. This speeds up the review process and reduces cost.
In a very real sense, the use of AI in the e-discovery work flow is like deploying an army of associates without having to pay for the army. Of course, AI is still no substitute for the intelligence of your lawyers in e-discovery production. AI can help your team get to a decision point faster and at a lower cost, but attorneys must still make the final decision about how to proceed in a given matter.
3. Improved Corporate Internal Investigations
One of the keys to a successful internal corporate investigation is the effective collection of data and other evidence, which forms the basis for the legal assessment of the situation. AI can significantly improve data collection efforts when it comes to digital forensics. And digital forensics is ripe for disruption. Investigators have an increasingly large and complicated pool of data to sift through, from e-communication and social media to video footage and smart sensors, and they have less time and budget to handle these increased demands.
“Digital forensics is an area that is becoming increasingly important in computing and often requires the intelligent analysis of large amounts of complex data,” concluded a study published by Digital Evidence & Electronic Signature Law Review. “It would therefore seem that AI is an ideal approach to deal with many of the problems that currently exist in digital forensics.”
AI technologies have already made their way into digital forensics and corporate internal investigations, even if they were not marketed as such. Sophisticated algorithms are used today for DNA sequence matching, crime detection and other use cases now in the works. Some researchers are exploring how AI can facilitate improved collaboration when it comes to the analysis of cybercrimes at any corporate location around the world. Others are experimenting with how AI can assist with recognizing patterns in the programming signatures of suspected criminals or wayward employees.
We’re still in the early stages of seeing how machine learning can bring greater efficiencies and deeper insights to digital forensics investigations, but the future of AI has clearly arrived, and we have a unique opportunity to put this technology to work.
Innovations are in the works now that will help corporate legal departments and their outside law firms to take AI to even greater heights of efficiency and productivity. In this next iteration, we’re likely to see it further leveraged as a predictive tool that anticipates emerging threats and risks to the corporate enterprise.
For example, AI will soon be able to help lawyers make better strategic decisions in litigation management, such as: How likely are we to be sued when we roll out a certain new product? Where are our greatest areas of vulnerability? How likely are we to win/lose a case if we choose to go to trial?
This is not about replacing people or squeezing more productivity out of a diminished workforce. As Thomas Davenport and Julia Kirby posit in their Harvard Business Review article “Beyond Automation”: "Augmentation … means starting with what humans do today and figuring out how that work could be deepened rather than diminished by a greater use of machines." They lay out several strategies, from moving up the cognitive hierarchy to diving into a specific niche, that people and companies can use to thrive in an era of smart machines.
The organizations that embrace and incorporate AI into their culture and capabilities will see a competitive advantage over their peers. Improved security, risk management and efficiency are already in reach, and strategic insights are right around the corner. The question is no longer if or when AI will be ready for mainstream use. The question now is whether one reaps its benefits or gets left behind.
Published April 5, 2017.