Chicago MEETUP – #LegalTech & Innovation Talks – March 8 at Skadden


Signup for a FREE TICKET to the next edition of the Chicago Legal Innovation + Technology Meetup at Skadden –  this THURSDAY, March 8 at 5pm.   

Speakers include:
Judy Perry Martinez , Innovation Center American Bar Association
Daniel B. Rodriguez , Dean Northwestern University Pritzker School of Law Tiffany Graves, Pro Bono Counsel Bradley Arant Boult Cummings LLP
Jack Newton, co-founder & CEO Clio – Legal Practice Management Software Jun Qiu, CPA, CPA, Chicago Kent College of Law

We look forward to seeing you there!

Six New Videos Added to TheLawLabChannel.com

WENDY RUBAS (VILLAGEMD) 
FROM ANECDOTE TO ANALYTICS: WAYFINDING AS A MODERN GENERAL COUNSEL

JILLIAN BOMMARITO (LEXPREDICT)
IT’S 10 PM – DO YOU KNOW WHERE YOUR LEGAL RESERVES ARE?

DENNIS KENNEDY (MASTERCARD)
AGILE LAWYERING IN THE PLATFORM ERA

EDDIE HARTMAN (LEGALZOOM)
THE PRICE IS THE PROOF

NICOLE SHANAHAN (STANFORD CODEX)
TRANSACTION COSTS AND LEGAL AI: FROM COASE’S THEOREM TO IBM WATSON, AND EVERYTHING IN BETWEEN

ED WALTERS (FASTCASE)
LAW’S FUTURE FROM FINANCE’S PAST: WHAT COULD POSSIBLY GO WRONG?

Crowdsourcing Accurately and Robustly Predicts Supreme Court Decisions — By Daniel Martin Katz, Michael Bommarito, Josh Blackman – via SSRN)

ABSTRACT:  Scholars have increasingly investigated “crowdsourcing” as an alternative to expert-based judgment or purely data-driven approaches to predicting the future. Under certain conditions, scholars have found that crowd-sourcing can outperform these other approaches. However, despite interest in the topic and a series of successful use cases, relatively few studies have applied empirical model thinking to evaluate the accuracy and robustness of crowdsourcing in real-world contexts. In this paper, we offer three novel contributions. First, we explore a dataset of over 600,000 predictions from over 7,000 participants in a multi-year tournament to predict the decisions of the Supreme Court of the United States. Second, we develop a comprehensive crowd construction framework that allows for the formal description and application of crowdsourcing to real-world data. Third, we apply this framework to our data to construct more than 275,000 crowd models. We find that in out-of-sample historical simulations, crowdsourcing robustly outperforms the commonly-accepted null model, yielding the highest-known performance for this context at 80.8% case level accuracy. To our knowledge, this dataset and analysis represent one of the largest explorations of recurring human prediction to date, and our results provide additional empirical support for the use of crowdsourcing as a prediction method.  (via SSRN)

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