The transatlantic debate over the final comma in a series — the so-called Oxford Comma — may have reached a decisive conclusion last week when a US judge cited the absence of the comma in a law governing overtime laws in the state of Maine as reason to find a case in favor of workers at Oakhurst Dairy who were suing for compensation.
Just to review for non-native English speakers and for Americans, the comma is innocuous. If you’re a Brit, it’s simply conventional to write a list of fruits and vegetables like this: “apples, oranges, bananas, tomatoes, and carrots.” In the United States, you would rarely see the comma between tomatoes and carrots — the “and” makes the comma’s appearance superfluous.
What is so remarkable about the decision is that it is by no means conventional to use an Oxford Comma in American grammar and it is often not taught. This author may have even thought it would be marked incorrect when he was in elementary school. The judge’s decision could have easily gone the other way. In this case, it’s possible the judge was influenced by more than just lazy legal writing, yet the importance of proofreading and considering all modes of legal interpretation was underscored by the incident.
Many startups have found the value in applying natural language processing advances to legal documents. Considering the uniformity of such things, one would think such law bots might actually be simpler than other forms of similar technology. But the ramifications of multiple interpretations are far more pronounced here. Getting automated contract analysis — or legislative analysis — correct is of paramount importance to all those involved.
Deloitte estimates more than 110,000 law jobs in just the United Kingdom alone could be gone within the next twenty years thanks to automation. That could result in the creation of more highly-skilled jobs and reduction of paralegal and temp positions, but the impact is undeniable.
“By 2025, we predict a profound transformation of the profession due to the quickening pace of technological developments, shifts in workforce demographics and the need to offer clients more value for money,” the report’s summary reads. The report itself claims “There is significant potential for high-skilled roles that involve repetitive processes to be automated by smart and self-learning algorithms.”
Around the world there have been scores of companies founded making an effort to grab market share. It is a nascent sector that might push NLP developers to the limits of linguistic understanding, forcing new developments in artificial intelligence that would anticipate possible future interpretations or challenges to the language presented in torts and bills.
Here are just five such startups from around the world trying to streamline the legal analysis process by pooling resources and applying new forms of machine intelligence to the field:
1. Acumenist Analytics / Lawbot.ai (Chennai)
“Lawyers spend too much of their precious time on contract reviews,” Lawbot Co-Founder Manaswani Krishna recently said. At the intersection of new automated legal services and chat bots, Acumenist has developed the simply-dubbed Lawbot that checks for legal loopholes, grammatical errors (such as missing the aforementioned Oxford commas, which have massive bearing on case law in any country), and other elements of contracts.
Founders Krishna and Krishna Sundaresan have bootstrapped operations so far and have begun rebranding their company after their flagship product. Once you upload your contract, the system scans the document for issues with the language and identifies possible indemnities or liabilities the contract as is might cause.
They are a member of the fourth cohort at the Target accelerator alongside companies like Preksh, Lechal, Uncanny Vision, and MintM.
2. Luminance (Cambridge)
Out of England’s University of Cambridge, Luminance announced a $3 million investment in September 2016 to speed up the analysis of legal documents, speeding up the process by learning how lawyers think along the way (which will unlock mysteries for a lot of people actually).
As Luminance Founder and CEO Emily Foges has said, “AI won’t replace lawyers, but can it make law fun again?” Rather than looking into the minutiae of contracts, Luminance will pick up on anomalies automatically. Therefore, lawyers will be a little freer to focus on actual negotiations and on their clients.
“This will transform document analysis and enhance the entire transaction process for law firms and their clients,” Foges has said. “Highly-trained lawyers who would otherwise be scanning through thousands of pages of repetitive documents can spend more of their time analysing the findings and negotiating the terms of the deal.”
3. Neota Logic
With its Neota Logic System (NLS), this company is automating a number of legal jobs like early case assessment and client intake. Their PerfectNDA product also promises to automate the filing and even the negotiation of non-disclosure agreements. They promise to make workflow more consistent for drafting, delivery, and archiving. They already claim several clients like the Florida Justice Technology Center, Hive Legal, Hall & Wilcox (firm), Littler, and Linklaters.
4. Nift.io (London)
Nift comes to us from London, co-founded by Stacey Seltzer and Meeta Gournay, CFO and partner at ‘innovation studio’ Prehype respectively. They have gotten their own contractual agreements out of Outbrain to go over its employment handbook, NatWest to analyze financial agreements, and a spot with BarkBox to go over freelance accords. When you upload a contract into Nift’s system, it ‘translates’ the text from legal mumbo jumbo into plain lay language with notes and explanations for readers.
“In such things as retail finance, but also housing, car rentals and so on, where people sign contracts, they are surprised by things later on. I think it’s bad for the individuals but it’s also bad for the business that they engage with,” Gournay recently told Legal Futures.
Seltzer added, “When you speak to individual lawyers or risk managers, I would argue that their intent is not to mislead [but] to do right by their organisation and by their customers. [Yet] law is complex and there is a very specific legal language… I think sometimes when you are in the profession, things that seem very clear to you are not necessarily very clear to people who are outside of it. I think that’s true for any profession.”
5. Ravn (London)
Applying machine learning and big data to a “sea” of legal data, Ravn uses enterprise-scale graph search to map legally relevant documents. They list four co-founders: CSO Peter Wallqvist, CTO Jan Van Hoecke, Professional Services Director Simon Pecovnik, and COO Sjoerd Smeets. Their software can either refine documents or extract information and restructure it into organized data sets. They recently released the RAVN ACE (Applied Cognitive Engine) bot to skim documents for GDPR compliance (General Data Protection Regulation).
— iManage RAVN (@iManageRAVN) March 9, 2017
“GDPR compliance is of universal importance as it will apply to any organisation that control and process data concerning EU citizens,” Wallqvist recently stated. “Using RAVN’s unique ACE technology, the GDPR Robot has the ability to deal with several aspects of the GDPR obligations in one platform: Auditing large volumes of structured and unstructured data, dealing with DSARs very efficiently, and finally to help review contractual obligations that are affected by the new regulations.”