On one hand, Machine Learning has been developing for the last couple of decades, and during the past years has been on the spotlight for several industries. On the other hand, legal profession is one of the most ancient fields in the world. So, how can we relate machine learning to the application of the law in these days?
About being a lawyer
Many people may think that being a lawyer is like being Harvey Specter or John Milton: Closing big deals, wearing glamorous suits in fundraisers and dazzling jurors with emotive speeches about reasonable doubt and guiltiness. That may be true for 1% of the lawyers around the globe, but most legal work includes long research hours, gathering and analyzing data for clients, and comparing probabilities when preparing for a case.
So, even if some lawyers obtain the spotlight in big cases matters, there is a team of hard-working paralegals and junior associates that work 14-16 hours a day to get the proper data to their superiors in order to prepare for big litigation cases or closing deals (every lawyer has gone through that process).
What can Machine Learning do for the lawyers?
Picture the scenario: 11:00 p.m. in the office and a Junior Partner comes to your paralegal cubicle and asks for a thorough research on jurisprudence and court precedents for - you name it -: discrimination of gender, merger control in the pharmaceutical business or [insert long and tedious topic here]. Of course, the deadline is 09:00 a.m. the next morning. The typical form of doing this is looking for a keyword of the case and then start reading all this (long) related cases until you figure, hopefully, a track of action before 09:00 am in the morning. Well, apparently, there is another way! A group of young guys graduated from a Stanford Law with a digital and more technological approach has developed a tool to make this job easier: Ravel Law. By using data visualization, analytics and machine learning, Ravel Law enables lawyers to find, contextualize, and interpret information that turns legal data into legal insights.
By this type of tool, based in machine learning, lawyers can save several hours preparing for cases and doing research. Ravel’s data base is huge and contains information of more than 5 decades of cases. What’s more, the filters that can be applied are really varied. As an example, two of the functions that this tool provides are (i) "Relevance", which aligns cases vertically, with the most relevant cases moving to the top of the screen, while (ii) “Ravel” organizes cases with a gravity model, so cases that are heavily cited by other cases in your search results will be the center of gravity and pull other cases towards them.
The use of this type of services definitely saves Firms a lot of (usually) non-billable hours, which can be addressed then working in another cases. Time is of the essence, especially when you work in a Law Firm, and machine learning is making the time management more effective for lawyers. As we can see, machine learning and the law are getting closer, and it should not surprise lawyers to have more and more of these tools later on in the future.
What does Suits have to do with this?
Not a lot actually. Besides that I really like the show, I just wanted to point out that seeing the likes of Harvey Specter in the big and small screen might be misleading to people not related to law practice; and thus, the reality is that not everybody that goes to Law School, can automatically become Harvey Specter (especially without researching (a lot!) first).
Picture: The black tie blog
About being a lawyer
Many people may think that being a lawyer is like being Harvey Specter or John Milton: Closing big deals, wearing glamorous suits in fundraisers and dazzling jurors with emotive speeches about reasonable doubt and guiltiness. That may be true for 1% of the lawyers around the globe, but most legal work includes long research hours, gathering and analyzing data for clients, and comparing probabilities when preparing for a case.
What can Machine Learning do for the lawyers?
Picture the scenario: 11:00 p.m. in the office and a Junior Partner comes to your paralegal cubicle and asks for a thorough research on jurisprudence and court precedents for - you name it -: discrimination of gender, merger control in the pharmaceutical business or [insert long and tedious topic here]. Of course, the deadline is 09:00 a.m. the next morning. The typical form of doing this is looking for a keyword of the case and then start reading all this (long) related cases until you figure, hopefully, a track of action before 09:00 am in the morning. Well, apparently, there is another way! A group of young guys graduated from a Stanford Law with a digital and more technological approach has developed a tool to make this job easier: Ravel Law. By using data visualization, analytics and machine learning, Ravel Law enables lawyers to find, contextualize, and interpret information that turns legal data into legal insights.
By this type of tool, based in machine learning, lawyers can save several hours preparing for cases and doing research. Ravel’s data base is huge and contains information of more than 5 decades of cases. What’s more, the filters that can be applied are really varied. As an example, two of the functions that this tool provides are (i) "Relevance", which aligns cases vertically, with the most relevant cases moving to the top of the screen, while (ii) “Ravel” organizes cases with a gravity model, so cases that are heavily cited by other cases in your search results will be the center of gravity and pull other cases towards them.
The use of this type of services definitely saves Firms a lot of (usually) non-billable hours, which can be addressed then working in another cases. Time is of the essence, especially when you work in a Law Firm, and machine learning is making the time management more effective for lawyers. As we can see, machine learning and the law are getting closer, and it should not surprise lawyers to have more and more of these tools later on in the future.
What does Suits have to do with this?
Not a lot actually. Besides that I really like the show, I just wanted to point out that seeing the likes of Harvey Specter in the big and small screen might be misleading to people not related to law practice; and thus, the reality is that not everybody that goes to Law School, can automatically become Harvey Specter (especially without researching (a lot!) first).
Picture: The black tie blog
Wow Ricardo, love this one!
ReplyDeleteGood job linking your expertise with the topic
ReplyDeleteThanks guys!! Really appreciate it!!
ReplyDelete