University of Oslo – Network Analysis and Machine Learning in Law Conference

Today I am in Oslo giving the Keynote Address at the University of Oslo – Network Analysis and Machine Learning in Law Conference. Some very cool papers have been will be presented –

Warren Agin Joins LexPredict as Director of Professional Development and Senior Consultant

We are very excited to welcome Warren E. Agin to the LexPredict team! Warren Agin joins LexPredict as Director of Professional Development and Senior Consultant. Warren is Founding Chair of the American Bar Association’s Legal Analytics Committee and serves Adjunct Professor at Boston College Law School where he teaches Legal Analytics. Warren is the author of a recent study which uses machine learning to predict outcomes in bankruptcy cases. Warren will bring nearly thirty years of legal practice experience to the LexPredict team.
#machinelearning #legaltech #legaldata #legalanalytics #legalinnovation

Network Analysis and Machine Learning in Law Conference – University of Oslo Faculty of Law – Call for Papers

Call For Papers:  “The empirical turn in legal scholarship has intensified with the integration of a new quantitative and computational methods. In our second annual workshop on law and social science methods, we call for papers on two increasingly popular approaches: network science and machine learning. We are especially interested in papers that seek to deepen the understanding of these methods or apply them to doctrinal or interdisciplinary questions in areas such as criminology, international law, corporate Law and sustainable development.

The Keynote Speaker for the workshop is Dan Katz, Illinois Tech – Chicago Kent College of Law, who has been a pioneer in the use of both methods in understanding and predicting the behavior of the US Supreme Court and advancing the field of legal technology”

Abstracts of approximately 200 words should be submitted to Martin Nøkleberg and Hanna Ahlström by  22 August 2018.

Acceptance of papers will be notified by 1 September 2018.
Papers should be submitted by 24 September 2018.
Workshop 10-11 October 2018 at the University of Olso 

OpenEDGAR: Open Source Software for SEC EDGAR Analysis (Michael Bommarito, Daniel Martin Katz & Eric Detterman)

Our next paper — OpenEDGAR – Open Source Software for SEC Edgar Analysis is now available.  This paper explores a range of #OpenSource tools we have developed to explore the EDGAR system operated by the US Securities and Exchange Commission (SEC).  While a range of more sophisticated extraction and clause classification protocols can be developed leveraging LexNLP and other open and closed source tools, we provide some very simple code examples as an illustrative starting point.

Click here for Paper:   < SSRN > < arXiv >
Access Codebase Here: < Github >

OpenEDGAR is an open source Python framework designed to rapidly construct research databases based on the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system operated by the US Securities and Exchange Commission (SEC). OpenEDGAR is built on the Django application framework, supports distributed compute across one or more servers, and includes functionality to (i) retrieve and parse index and filing data from EDGAR, (ii) build tables for key metadata like form type and filer, (iii) retrieve, parse, and update CIK to ticker and industry mappings, (iv) extract content and metadata from filing documents, and (v) search filing document contents. OpenEDGAR is designed for use in both academic research and industrial applications, and is distributed under MIT License at

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.