I am pleased to serve as a Program Chair and Speaker at the Plenary Presidential Summit @ New York State Bar Association Annual Meeting. Today’s topic will be Artificial Intelligence and its Impact on the Legal Profession. Joining me on the panel are the following panelists covering the following topics:
What is Artificial Intelligence? What is Machine Learning?
Dera J. Nevin, eDiscovery Counsel, Proskauer
What are Some Applications of Artificial Intelligence, Machine Learning, and Predictive Analytics in Law?
Andrew M.J. Arruda, CEO & Co-Founder, Ross Intelligence
Daniel Martin Katz, J.D., Ph.D., Associate Professor of Law, Illinois Tech – Chicago Kent Law
What are the Labor Market Impacts? More Jobs, Less Jobs, Different Forms of Legal Jobs and Legal Work?
Noah Waisberg, J.D., Co-founder & CEO, Kira Systems
This week we kicked off the semester for our @ChicagoKentLaw / @SeyfarthShawLLP Legal Process Improvement / Legal Project Management Class. This 15 week two credit class will be among the very first of its kind to be taught at a law school.
I am very honored to have the opportunity to work with the Seyfarth Lean Consulting team – Kim R. Craig, Larissa Kruzel, Kyle Hoover on this course! #leanlaw #legaltech #sixsigmaforlawyers #LPM #legalprocessimprovement #legalengineering
Long time coming for us but here is Version 2.01 of our #SCOTUS Paper …
We have added three times the number years to the prediction model and now predict out-of-sample nearly two centuries of historical decisions (1816-2015). Then, we compare our results to three separate null models (including one which leverages in-sample information).
Here is the abstract: Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. Our model leverages the random forest method together with unique feature engineering to predict nearly two centuries of historical decisions (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an advance for the science of quantitative legal prediction and portend a range of other potential applications.
Enjoyed delivering the Keynote Address at the #FutureOfLaw Conference here at Aviva Stadium in Dublin, Ireland.
Thanks to Leman Solicitors and all of the sponsors for this wonderful event!
I am excited to speak tomorrow at the 2016 General Counsel Summit in London presented by The Economist Magazine.
It will be a short trip to London for me as I will be flying back on Thursday in time for final preparations for the Fin(Legal)Tech Conference that I am helping organize November 4th 2016 at Illinois Tech – Chicago Kent College of Law.
I look forward to seeing folks in London and Chicago later this week!
When it comes to prediction – law would benefit from better applying the tools of STEM / Finance / Insurance and so in that spirit — our company recently launched LexSemble and it allows for near frictionless crowd sourcing of predictions in law (and beyond). Many potential applications in law including early (and ongoing) case assessment in litigation, forecasting various sorts of transactional outcomes and predicting the actions of regulators, etc. It also has a range of machine learning capabilities which allow for crowd segmentation, expert weighting, natural language processing on relevant documents, etc.
Learn More: https://lexsemble.com/features.html