Law.com Subscribers SAVE 30%
Call 855-808-4530 or email [email protected] to receive your discount on a new subscription.
Predictive Analytics in a Data-Driven World
The use of predictive coding in e-discovery answers the need to automate document review for discovery purposes. Why is automation of this process now so necessary? Because Big Data is upon us, and the legal profession is as much affected by this mountain of information as is business. We need automation to make our way through this.
Prediction coding is one part of the process of quantitative predictive analytics, and the use of predictive analytics in the practice of law is charging well past its role in e-discovery. Think about it: how often is a lawyer required to make a prediction: Do I have a case? What is our likely exposure? How should this legal matter be priced? Who in the firm is best equipped to handle it? What's up with the judge? What's the best jury composition? These are the questions that can, and will, rely on human-trained technology designed to wrest these decisions from the frailties of human thinking.
Daniel Katz, assistant professor of law at Michigan State University College of Law, and co-founder of Reinvent Law Laboratory, explains that the circumstances creating this environment include the monumental increase of Big Data, the vast decrease in data storage costs, and advancements in computing power and learning. ReinventLaw identifies four pillars of innovation for the legal services industry: Law+Tech+Design+Delivery. As counsel and legal consumers act to drive down costs ' demanding value propositions and reduction of legal spend, the use of predictive models in legal analysis has fertile ground on which to develop and grow.
Access to large bodies of unstructured and semi-structured information is growing, but the real question is: How do lawyers leverage the availability of this information in a useful way?
'Law firms don't think of themselves as data-driven,' says Professor Katz. 'They don't consider saving data across all their sources of information. But that's what modern management is about; they start by collecting data ' massive amounts of information ' and use it to generate sophisticated predictive models as a basis for making decisions.'
The most disruptive of all possible displacing technologies ' quantitative legal prediction ' can enable this process in the practice of law, and is likely to drive a substantial amount of the future innovation in the legal services industry.
How Does Predictive Analytics Work?
Predictive analytics is a blend of tools and techniques that enable organizations to identify patterns in data that can be used to make predictions of future outcomes. In business, predictive analytics typically take the form of predictive models that are used to drive better decision making. They find and measure patterns to identify risks and opportunities using transactional, demographic, Web-based, historical, text, sensor, economic and unstructured data. These powerful models are able to consider multiple factors and predict outcomes with a high level of accuracy.
The three functions of predictive analytics are:
- Pattern detection;
- Differentiation; and
- Tying together relevant data from different data sets to form a conclusion.
In order to make a prediction regarding any number of unknown outcomes, a lawyer must examine data with an eye toward detecting patterns of behaviors and outcomes. This data arises from a variety of data sets that are often impossible to integrate for the purpose of comparative relevancy (e.g., social media data, court information, legal precedent, etc.), and present a variety of data types: structured, semi-structured and unstructured. By repetitively examining this data, lawyers, researchers and IT developers begin to learn and apply that knowledge across different classes of problems and reason by analogy. The exercise then is to create algorithms and incorporate data mining techniques to train computational functions to do the same thing, but with more ease and in less time.
e-Discovery is the first component of the legal process to experience a great deal of success in creating reliable outcomes by using keywords and coding methods that form relational bases. This enables pattern detection and differentiation among various types of data.
However, 'it is not correct to believe that it can't apply to other classes of problems, like what's going to happen in a case,' Professor Katz says. 'What are the features of a personal injury case that will drive the outcome? This insight can be obtained through data. Reasoning has always been the centerpiece of how legal judgments are made. The development of algorithms and use of data-mining techniques are making incursions into the industry, but you must look outside the industry to see how other it is being used in other spaces.'
Conclusion
In the end, predictive modeling will be limited only to the extent it relies on the limits of human creativity and analysis in enabling it to mimic the behavior of 'expert reasoners': What does it mean to 'think like a lawyer?'
While humans are amazing in their abilities to detect patterns, aggregation is a problem. How much data can a person consider? Machines have no such restrictions. They are not limited by the failings of memory loss, subjective perspectives, or narrow vision. They can proceed to perform high-level pattern detection, high dimensional similarity matching and analogical reasoning to produce predictions that will be reliable, at a fraction of the cost and time.
The use of predictive coding in e-discovery answers the need to automate document review for discovery purposes. Why is automation of this process now so necessary? Because Big Data is upon us, and the legal profession is as much affected by this mountain of information as is business. We need automation to make our way through this.
Prediction coding is one part of the process of quantitative predictive analytics, and the use of predictive analytics in the practice of law is charging well past its role in e-discovery. Think about it: how often is a lawyer required to make a prediction: Do I have a case? What is our likely exposure? How should this legal matter be priced? Who in the firm is best equipped to handle it? What's up with the judge? What's the best jury composition? These are the questions that can, and will, rely on human-trained technology designed to wrest these decisions from the frailties of human thinking.
Daniel Katz, assistant professor of law at
Access to large bodies of unstructured and semi-structured information is growing, but the real question is: How do lawyers leverage the availability of this information in a useful way?
'Law firms don't think of themselves as data-driven,' says Professor Katz. 'They don't consider saving data across all their sources of information. But that's what modern management is about; they start by collecting data ' massive amounts of information ' and use it to generate sophisticated predictive models as a basis for making decisions.'
The most disruptive of all possible displacing technologies ' quantitative legal prediction ' can enable this process in the practice of law, and is likely to drive a substantial amount of the future innovation in the legal services industry.
How Does Predictive Analytics Work?
Predictive analytics is a blend of tools and techniques that enable organizations to identify patterns in data that can be used to make predictions of future outcomes. In business, predictive analytics typically take the form of predictive models that are used to drive better decision making. They find and measure patterns to identify risks and opportunities using transactional, demographic, Web-based, historical, text, sensor, economic and unstructured data. These powerful models are able to consider multiple factors and predict outcomes with a high level of accuracy.
The three functions of predictive analytics are:
- Pattern detection;
- Differentiation; and
- Tying together relevant data from different data sets to form a conclusion.
In order to make a prediction regarding any number of unknown outcomes, a lawyer must examine data with an eye toward detecting patterns of behaviors and outcomes. This data arises from a variety of data sets that are often impossible to integrate for the purpose of comparative relevancy (e.g., social media data, court information, legal precedent, etc.), and present a variety of data types: structured, semi-structured and unstructured. By repetitively examining this data, lawyers, researchers and IT developers begin to learn and apply that knowledge across different classes of problems and reason by analogy. The exercise then is to create algorithms and incorporate data mining techniques to train computational functions to do the same thing, but with more ease and in less time.
e-Discovery is the first component of the legal process to experience a great deal of success in creating reliable outcomes by using keywords and coding methods that form relational bases. This enables pattern detection and differentiation among various types of data.
However, 'it is not correct to believe that it can't apply to other classes of problems, like what's going to happen in a case,' Professor Katz says. 'What are the features of a personal injury case that will drive the outcome? This insight can be obtained through data. Reasoning has always been the centerpiece of how legal judgments are made. The development of algorithms and use of data-mining techniques are making incursions into the industry, but you must look outside the industry to see how other it is being used in other spaces.'
Conclusion
In the end, predictive modeling will be limited only to the extent it relies on the limits of human creativity and analysis in enabling it to mimic the behavior of 'expert reasoners': What does it mean to 'think like a lawyer?'
Major Differences In UK, U.S. Copyright Laws
This article highlights how copyright law in the United Kingdom differs from U.S. copyright law, and points out differences that may be crucial to entertainment and media businesses familiar with U.S law that are interested in operating in the United Kingdom or under UK law. The article also briefly addresses contrasts in UK and U.S. trademark law.
The Article 8 Opt In
The Article 8 opt-in election adds an additional layer of complexity to the already labyrinthine rules governing perfection of security interests under the UCC. A lender that is unaware of the nuances created by the opt in (may find its security interest vulnerable to being primed by another party that has taken steps to perfect in a superior manner under the circumstances.
Strategy vs. Tactics: Two Sides of a Difficult Coin
With each successive large-scale cyber attack, it is slowly becoming clear that ransomware attacks are targeting the critical infrastructure of the most powerful country on the planet. Understanding the strategy, and tactics of our opponents, as well as the strategy and the tactics we implement as a response are vital to victory.
Legal Possession: What Does It Mean?
Possession of real property is a matter of physical fact. Having the right or legal entitlement to possession is not "possession," possession is "the fact of having or holding property in one's power." That power means having physical dominion and control over the property.
The Stranger to the Deed Rule
In 1987, a unanimous Court of Appeals reaffirmed the vitality of the "stranger to the deed" rule, which holds that if a grantor executes a deed to a grantee purporting to create an easement in a third party, the easement is invalid. Daniello v. Wagner, decided by the Second Department on November 29th, makes it clear that not all grantors (or their lawyers) have received the Court of Appeals' message, suggesting that the rule needs re-examination.