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 the disruption AI in 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.
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.
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).
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.
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
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.
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
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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