Take-away #2 :
The applications of artificial intelligence to legal industry tasks is robust in three areas where the relevant data is publicly available.
In Part I of this series I wrote that the use of artificial intelligence (AI) to predict outcomes in civil litigation isn’t happening any time soon because the necessary data is held mostly in fragmented silos. The relevant data is available from proprietary sources only — you need a law firm’s or company’s permission to access it — and you’re unlikely to get it.
In this Part II I turn to applications for AI to tasks where the relevant data is publicly available.
These applications are further along in development. They’re not yet the subject of widespread adoption (see my post on slow adoption of legal technology).
Legal Research:
Case law, statutes, and regulations are all publicly available. So a handful of tech firms are already applying AI to legal research.
Getting the most widespread attention in this group is Ross Intelligence,
which uses natural language in connection with IBM’s Watson to search cases, statutes and regulations to provide legal information ranging from citations to full legal briefs.
In addition, established incumbents in the legal research field — Lexis Advance and Thomson Reuters / Westlaw — have begun to roll out AI-powered features.
An early adopter: Salazar Jackson law firm in Coral Gables, Florida — emphasizing bankruptcy and related corporate work. Six lawyers and two paralegals and founded by Luis Salazar — former partner with Greenberg Traurig:
“‘I’m a bankruptcy lawyer with 20 years of experience and if I spent the time and had done my own research, ROSS’s responses would be about the same as I could do.
“ROSS is the firm’s legal researcher for US bankruptcy law. We go to ROSS first and then if we need to we can go a bit deeper.”
Document Review:
Another traditional assignment for young law associates: Each category of document review concerns materials that are readily available to the target company, litigant, or corporation involved:
- M&A due diligence: Reviewing target company documentation and use in post-merger integration. Axiom’s Contracts Intelligence Platform announced January 30, 2018 is the most recent. See Kira Systems business was and LexPredict’s new ContraxSuite as well.
- Pretrial discovery in litigation: Disclosure of documents among parties to a lawsuit. See Brainspace and NextLP.
- In-house contract review: Law departments managing their companies’ contracts. See LawGeex, and Luminance.
Applications from Kira Systems and Evisort apply across the above three categories.
Legal Analytics:
The most succinct description of legal analytics I’ve found is by a tech journalist in Silicon Valley several years ago — writing about LexMachina:
“The technology behind LexMachina compiles data and documents from court cases and converts them into searchable text files. Then, when a keyword, patent or party is searched for, data and documents are sent back out. It can give lawyers more information on specific judges, a client’s history and most importantly, information on what they can do to have a better chance at winning.”
Legal analytics begins with publicly available data — judicial opinions and court records. But its salient insights come from digging up information under one of two categories:
- Querying judicial opinions and court records about issues relevant to success or failure in a case. Such as: Has my judge presided over cases involving opposing counsel in my case — and how has he or she ruled — is there a pattern?
- Taking information included in the public record and researching other databases for further insight. Did my judge when in private practice represent companies in the same industry — or represent parties in conflict with such companies?
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See Part I and Part III of this series.