Since AI is quite a debatable topic, the community of professionals across industries has mixed feelings on the outcomes of AI application for humans. Nonetheless, there are very successful AI companies applying the power of machines, and investors are supporting them.
AI is most often associated with wealth management as it seems to be the segment where AI could do the most damage and cause the strongest disruption. However, AI still finds its way into other industries, like applications in InsurTech. Aside from the industries mentioned, there are also expectations on AIs being successfully and beneficially applied in law enforcement. One of the recent reports—Artificial Intelligence in Law: The State of Play 2016 by Michael Mills—explores five ways AI can be applied in law.
Legal research
Research is not exclusively an area of AI application in law. Contextual information discovery is a valuable skill machines can learn and significantly ease the work for humans. There are projects backed by large corporations. The report cites examples of IBM Watson, Westlaw, Clio, Ravel Law, Bloomberg BNA, and ROSS Intelligence. Some projects have applied NLP techniques to legal research for more than a decade already.
Even though some corporations have been applying AI in legal research for a while, it is a complex task because AIs can’t do all the magic. It requires the investment of time, human expertise, and significant efforts to assemble useful data sets, analyze the content, train the algorithms and test the results, as Mr. Mills states. Depending on the depth of the research topic, AI needs tuning and a different level of human expertise to interpret the findings and connect the dots.
Electronic discovery
NLP and machine learning can be used in technology-assisted review (TAR, or predictive coding) to brush through massive data sets for e-discovery. Companies like Recommind, Microsoft’s Equivio, kCura’s Content Analyst, and others represent the set of players in the space that either develop or license solutions for e-discovery.
According to the report, predictive coding is faster, better, cheaper, and more consistent than a human-powered review. However, a human still has a valuable place near the machine since technology needs to be assisted. Highly skilled and knowledgeable lawyers need to train the machine and transmit the case’s details to make e-discovery more relevant and accurate. It is also important to have the correct lawyers working with AI since interpreting the results requires expertise and analytical thinking.
As a result, lawyers are empowered but not replaced—at least until the moment when machines can reach the intelligence level of a human lawyer and have knowledge of the details of the case and develop a certain hunch on cases and investigation.
Self-service compliance
AI can be applied to provide fact- and context-specific answers to legal, compliance, and policy questions. Solutions like Neota Logic and ComplianceHR aim to assist professionals in evaluating independent contractor status, overtime exemption, and other law issues.
Contract analysis
Machine learning, NLP, and other AI techniques can be applied to various aspects of the lifecycle of contracts. AI can assist in risk management related to contracts by helping professionals understand and manage the rights, obligations, and risks in a company’s contracts. As a result, lawyers can rationalize the contract initiation processes, negotiations, contract drafting, and assist in managing the contracts from initiation through execution to their expiration.
Kira Systems is one of the companies working in the space that applies machine learning to help enterprises uncover relevant information from unstructured contracts and related documents.
KMStandards is another example. The company offers patented software that allows professionals to build model forms from the company’s own agreements, audit entire contract sets, and quickly review incoming contracts.
There are also companies like RAVN, Seal Software, and others successfully applying AI in contract analysis to assist enterprises in mitigating the risks and costs related to contracts.
Predictive modeling
The ability to foresee the possible outcomes of cases based on all available details and information would significantly cut the costs of litigations and court time. One of the examples of companies applying data mining and predictive analytics techniques to forecast outcomes of IP litigation is Lex Machina, which was also mentioned in the report. The company provides legal analytics to companies and law firms, promising them to craft successful strategies, win cases, and close business.
But the innovators from Lex Machina went further with Motion Kickstarter. This solution helps attorneys compare the arguments and motion styles that have been successful before a specific judge. The attorney can enter a judge’s name and motion type and instantly view the judge’s recent orders on that motion type as well as the briefing that led up to those orders. Lawyers can then compare granted motions with denied motions to see what has worked and what has not to create a winning motion strategy or approach.
Sky Analytics is another player in the field. It aggregates all of the company’s outside legal spends into an easy-to-understand platform. It presents unique views into the company’s data revealing new savings opportunities and helping increase value from counsel outside.
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