In this special guest feature, Cat Casey, DISCO Chief Innovation Officer, focuses on how AI and machine learning are empowering the practice of law. Catherine “Cat” A. Casey is an established thought leader in ediscovery and data analytics. Cat has more than a decade of experience working with organizations around the complex ediscovery and forensic needs that arise from litigation, expansive regulation and contractual relationships, and works closely with DISCO clients as they modernize the practice of law. She is a graduate of Harvard University, and also attended Pepperdine School of Law.
Artificial intelligence and automation are top of mind for legal practitioners today, with frequent articles and reports coming out about AI disrupting law. A recent Gartner report found that AI and automation could eliminate 1.8 million jobs by 2020. While this is a terrifying thought, it is also estimated that 2.3 million new jobs will be created in the same timeframe. In fact, Gartner estimates that AI could drive $2.9 trillion in business value as early as 2021 — and that is just the beginning.
If you believe the hype, we are on the verge of AI-powered robo-laywers replacing legal practitioners completely. In reality, we are far from that level of legal AI sophistication. The augmented intelligence models deployed today in the field of law are more focused on amplifying human decision making than replacing it.
Should legal practitioners be afraid… be very afraid?
Simply answered, no. In fact, this legal technology revolution offers massive opportunities for practitioners to thrive if they adapt.
Rather than seeking to replace and displace humans, augmented intelligence instead seeks to marry human and advanced technology to amplify and expands the impact of human decisions. Ultimately, augmented intelligence frees up legal professionals to focus on higher cognition tasks, while automating discrete repeatable tasks.
What does augmented intelligence mean for the practice of law?
Multiple tools are bringing augmented intelligence to legal practitioners. The key areas of focus today are ediscovery, contract analytics, and knowledge management (KM).
Ediscovery and CAL
In ediscovery, the preliminary fact-finding stage prior to litigation or an investigation, the use of machine learning (known as technology assisted review or TAR) is deployed to reduce time to evidence in large data sets. The newest iteration of TAR leverages reinforcement learning models called continuous active learning (CAL).
Using CAL, each decision an attorney makes when reviewing a document is amplified across the entire dataset and the data is reprioritized bringing the most relevant information forward. This yields substantially faster (over 50% acceleration) and more accurate results than relying only on humans, freeing up attorneys to focus on case development.
Contract Analytics
Machine learning and natural language processing are deployed across large contract portfolios to reduce the time necessary to compare contract clauses to find anomalies, facilitate due diligence, or predictively suggest language in contract generation. Legal professionals can quickly identify information from current and legacy contracts to ensure that key contractual language and commitments are not missed.
While the efficacy of this type of tool is highly dependent on cleanliness of data and a manmade taxonomy to inform the technology, there are many benefits to the practice of law. These include increasing consistency across similar contracts, improving compliance, identifying atypical clauses, identifying opportunities to increase revenue, and reducing costs for contract review and generation.
Knowledge Management
More art than science, KM is the process of capturing and reusing legal know-how and work product or identifying colleagues with relevant experience. While most major law firms today have some sort of document management system (DMS), these systems are challenging to extract insight from due to the volume of data. Machine learning and AI are being deployed to accelerate law firms’ extraction of these types of insights and value from the content they produce. Advanced machine learning facilitates more accurate retrieval in lieu of relying on unpredictable manual human retrieval and review.
Iron Man, Esquire
The difference between AI and augmented intelligence is akin to the Terminator vs. Iron Man. The former has fully autonomous machines, the latter is comprised of human-centric application of technology that is dependent on a Tony Stark to execute. The practice of law is more Iron Man than Terminator — technology is accelerating time to insight and informing decision-making but is ultimately dependent on human input. AI, deep learning and data visualization work with legal practitioners to substantially reduce time to insight, allowing attorneys to focus on the practice of law.
While skynet is not replacing attorneys anytime soon, or ever, Augmented Intelligence is changing the practice of law. Rather than the mundane and tedious, lawyers today and in the future can focus on the substantive and intellectually engaging aspects of law that brought them to law school in the first place.
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