Workforce Implications of Machine Learning – Brynjolfsson + Mitchell in Science

Regarding the quote above — we agree.  However, it should be noted that the ‘simple substitution story’ works at the aggregate level over a period of time with the simple assumption that the tasks which comprise current jobs can be decomposed and recombined into new jobs.  Certainly, institutions (both firms and public sector) will take some period of time to be able to repackage certain existing jobs.  Thus, lags are to be expected.  < Click Here to Access the Article >

Applied Introduction to Machine Learning (via International Legal Technology Association Blog)

Fish & Richardson is one of the largest IP firms in the US so it is cool to see them exploring these ideas.  If you look at this intro using Microsoft Azure – this is very on point with lots of we have been saying about the mix of semistructured data and #MLaaS (machine learning as a service) … and why we teach both an introduction to quant methods and a machine learning for lawyers course.

International Bar Association – President’s Task Force on the Future of Legal Services (Phase I)

I am very happy to be included among most cited scholars in the network of the INTERNATIONAL BAR ASSOCIATION – PRESIDENT’S TASK FORCE ON THE FUTURE OF LEGAL SERVICES along with John Flood, the late Larry Ribstein, Richard Susskind, Paul Lippe, Tanina Rostain, William Henderson among many others … 

Exploring the Use of Text Classi€cation in the Legal Domain (via arXiv)

ABSTRACT:  In this paper, we investigate the application of text classi€cation methods to support law professionals. We present several experiments applying machine learning techniques to predict with high accuracy the ruling of the French Supreme Court and the law area to which a case belongs to. We also investigate the inƒuence of the time period in which a ruling was made on the form of the case description and the extent to which we need to mask information in a full case ruling to automatically obtain training and test data that resembles case descriptions. We developed a mean probability ensemble system combining the output of multiple SVM classi€ers. We report results of 98% average F1 score in predicting a case ruling, 96% F1 score for predicting the law area of a case, and 87.07% F1 score on estimating the date of a ruling

The Road Less Traveled – New Law, T-Shaped Lawyers and the Path to Law School { Product + Market} Fit

This is my Presentation for the NALP Conference on Emerging Legal Careers.

Honored to Deliver the Keynote Address at the NALP Summit on Emerging Careers for Law Grads

Honored to deliver the keynote at yesterday’s NALP Summit on Emerging Careers for Law Grads