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5 Steps on the Road to Leveraging Machine Intelligence in Legal

5 Steps on the Road to Leveraging Machine Intelligence

Note: The following is a guest post from Daryn Teague, who provides support to the litigation software product line within the LexisNexis software division.

Lawyers and legal technology professionals are struggling to grasp the vast potential of “machine intelligence” to reinvent the way we litigate in the U.S. A 2011 article in the New York Times reported on the broad sense that we were on the cusp of a game change with machine intelligence in eDiscovery, but four years later there are still lots of questions about precisely how and when this revolution may take place.

As explained in a report earlier this year from Blue Hill Research: “Machine learning refers to the capacity of software to automatically adjust its performance and operations based on the consideration of past results, pattern recognition, and user feedback to predefined rules and heuristics. As such, applications of machine learning involve a legal intelligence engine that automatically improves and recalibrates with use.”

The most common initial applications of machine intelligence appear to be predictive coding and technology-assisted review in eDiscovery. However, a big question is how litigation teams can get started down a path that might lead to a return on the big promise.

In the May 2015 meeting of The Legal Innovation 2020 Working Group (LI 2020) – an invitation-only peer group focusing on innovations in technology and workflow that shape the eDiscovery, litigation technology and legal information management industries – thought leaders from both corporate legal departments and law firms discussed some incremental first steps that law firms and their clients should consider as they chart a course on the road to machine intelligence:

1. Change management – machine intelligence is innovative by design so it’s likely to be quite disruptive to the organization, requiring very thoughtful management of change to staffing, talent needs, etc.

2. Metrics and measurement – it’s important to build an entire culture from day one that is committed to a clear sense of using consistent metrics to measure progress toward goals (e.g., speed, accuracy, costs, etc.).

3. Better use and understanding of analytics – initiate a conversation now with your key stakeholders (e.g., leadership, law firm partners, clients, IT staff, etc.) to put in place the best analytics possible.

4. Test cases and use scenarios – define a clear use scenario (i.e., a simple narrative for a typical use case of the technology) to help you explore the set of tasks and interactions required for your process design.

5. Client demand – try to gauge where your clients are most focused (e.g., cost? accuracy? speed?) and assess their expectations from the use of such cutting-edge technology for performing highly technical litigation-related tasks.

The LI 2020 Working Group gathers for monthly virtual meetings in order to be engaged and educated on evolving trends that impact legal business models. The LI 2020 initiative is managed by The Cowen Group and is underwritten by LexisNexis.

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4 Key Implications of Early Case Assessment Technologies

Photo credit: Flickr, Dr. Wendy Longo, (CC BY-ND 2.0)

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About Contributing Writer

Contributing Writer
This bio page is used to publish submissions by contributing writers. We welcome contributions from the legal community and are especially keen for contributions from our customers. Please review previous submissions published here and the “About Us” section to get a sense for what topics work for this blog. All posts must be original content not published elsewhere for at least 30 days. To submit an idea for consideration, please email blsssocial@lexisnexis.com.
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