Peer to Peer Magazine

Summer 2019: Part 1

The quarterly publication of the International Legal Technology Association

Issue link: http://epubs.iltanet.org/i/1136335

Contents of this Issue

Navigation

Page 27 of 67

P E E R T O P E E R : I L T A ' S Q U A R T E R L Y M A G A Z I N E | S U M M E R 2 0 1 9 29 other academic disciplines. Some of the AI progress from these endeavors will be generically applicable in any problem, so the legal industry will benefit. Who's leading the way in legal? The American Bar Association (ABA) released a report indicating 10% of respondents used artificial intelligence-based tech tools for their legal work in 2018. The larger the firm, the more likely they reported using AI; 35% of firms with 500+ lawyers used AI, compared to just 4% from firms employing two to nine attorneys. There are weekly announcements by law firms piloting Legal AI. And there are daily announcements by Legal AI vendors initiating efforts to harness the AI wild beast for legal. The law firm and vendor leaders who step back and focus their efforts strategically, developing real use cases, adoption strategies and "proof" that their Legal AI works, will succeed. Efficiency and Predictability A broad use for Legal AI is to streamline, enhance, or extend the delivery of legal services. Leveraging Legal AI for operations teams addresses real business problems. Helping define repeatable processes to operationalize a practice and scale legal services with ease is a laudable goal for implementing Legal AI. The "work" these tools perform holds the promise of replacing junior attorneys. At least that's what the AI vendors tell us. Where we are today is not there. Those efficiency tools can also introduce new errors, so there are risks there. In terms of adding value, efficiency tools can't really compete with predicting and avoiding the issue in the first place. The second broad use of Legal AI is in predictive tools that may do something you are not currently doing, like identifying risk patterns. There's less risk in getting information that you wouldn't otherwise have. The challenge there is responding to the results. You might not understand what you get. Tools that predict case outcomes, how judges will rule, what jurisdictions tend to be best, what risks to avoid, etc. are valuable and address business challenges. But many predictive results aren't even verifiable except at a very macro level. Other Legal AI Uses IT teams are not generally pressured to use databases more but when a database is coupled with Legal AI, then it becomes something. Ediscovery was the first sandbox for the application of AI-enabled tools, using AI to accelerate document review during the discovery phase of litigation. Mergers and acquisitions are another area where AI-enabled technologies support contract review during the due diligence phase. Contract analysis is the latest area to benefit from the capabilities enabled by machine learning. More nascent uses include blending predictive analytics and its algorithms, firm- specific intellectual property, and AI-enabled tools to create new proprietary tools. It's a struggle to categorize the various "types" of Legal AI available today. There are cognitive services, image recognition, deep learning and others. Blickstein Group research has begun to organize the types of business problems we can apply AI techniques to in order to achieve some desired outcome, as follows: • Billing/Spend Management • Contract – Pre-Execution & Post- Execution • Document Management • Ediscovery/Document Review • Expertise Automation • Insight/Predictive Tools • Legal Research • Litigation Management • Other Tools Different Learning Styles? How much do we as consumers of Legal AI, need to understand what AI is or how the underlying technologies work? We've seen AI defined as using natural language processing (NLP) and machine learning (ML) to achieve a cognitive task, such as reading a text, or other similar techniques related to pattern recognition and computational linguistics, that are spotting certain semantic features, and then telling the user what it has found (https://www.artificiallawyer.com/2019/04/17/ legal-ai-its-definition-and-its-value-to- the-legal-world-easter-repost/). That gets complicated by the variety of underlying technologies used in Legal AI. A way to think of this is there are different "learning styles" We've seen AI defined as using natural language processing (NLP) and/or machine learning (ML) to achieve a cognitive task, such as reading a text, or other similar techniques related to pattern recognition and computational linguistics, that are spotting certain semantic features, and then telling the user what it has found.

Articles in this issue

Links on this page

Archives of this issue

view archives of Peer to Peer Magazine - Summer 2019: Part 1