This intro class is designed to train students to efficiently manage, collect, explore, analyze, and communicate in a legal profession that is increasingly being driven by data.
Our goal is to imbue our students with the capability to understand the process of extracting actionable knowledge from data, to distinguish themselves in legal proceedings involving data or analysis, and assist in firm and in-house management, including billing, case forecasting, process improvement, resource management, and financial operations.
This course assumes prior knowledge of statistics, such as might be obtained in Quantitative Methods for Lawyersor through advanced undergraduate curricula. This class is not for everyone; for many, it will prove to be challenging. With that warning, we encourage you to consider your interest and career aspirations against the unique experience and value of this class. To our knowledge, this is the only existing class that teaches these quantitative skills to lawyers and law students.
Still in beta – we will be adding much more to this site as we move forward!
From the Abstract: “This Article proposes a novel and provocative analysis of judicial opinions that are published without indicating individual authorship. Our approach provides an unbiased, quantitative, and computer scientific answer to a problem that has long plagued legal commentators. Our work uses natural language processing to predict authorship of judicial opinions that are unsigned or whose attribution is disputed. Using a dataset of Supreme Court opinions with known authorship, we identify key words and phrases that can, to a high degree of accuracy, predict authorship. Thus, our method makes accessible an important class of cases heretofore inaccessible. For illustrative purposes, we explain our process as applied to the Obamacare decision, in which the authorship of a joint dissent was subject to significant popular speculation. We conclude with a chart predicting the author of every unsigned per curiam opinion during the Roberts Court.” <HT: Josh Blackman>
Today I have the pleasure of serving as the Keynote Speaker at LegalWeek Strategic Technology Forum at the Grand Hotel Palazzo della Fonte just outside of Rome. This is a very intimate gathering of the managing partners and/or chief technology officers of the some of the world’s largest law firms. Participating law firms include but are not limited to: Allen & Overy, Linklaters, Freshfields Bruckhaus Deringer, Hogan Lovells, Ashurst, Berrymans Lace Mawer, Berwin Leighton Paisner, Bird & Bird, Irwin Mitchell, Charles Russell, Herbert Smith Freehills, RPC, DAC Beachcroft, AKD, DWF, Lewis Silkin, Nabarro, SJ Berwin, Taylor Wessing, Trowers & Hamlins, Mayer Brown, Al Tamimi & Company, Thrings, CMS Derks Star Busmann, CMS Hasche Sigle, Cuatrecasas, Gonçalves Pereira, Fidal, Kromann Reumert, Latham & Watkins, Leigh Day & Co, Osborne Clarke, Perkins Coie, Pinsent Masons, Riverview Law, Skadden, Arps, Slate, Meagher & Flom.
This semester here at Michigan State University College of Law, I am team teaching E-Discovery together with my colleague Adam Candeub. For a number of reasons, I enjoyed this video as it highlights the real gap in knowledge that exists between the tech infused Lawyer for the 21st Century and everyone else. The future belongs to the former and the time to acquire those skills is now!
Here is the description: “Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.” Check it out! TOC ML4Hackers