American Bar Association
Section of Science & Technology Law: Big Data

Section of Science & Technology Law:
Big Data

Message From The Chair

This committee guides the legal profession into best practices to meet the challenges imposed by big data. We continue to seek leaders from verticals in law and technology to devote their time, energy and resources to further the interest of data science in law. Thank you.

Who We Are

“Big data” is defined as data of sufficient volume, complexity or velocity that it exceeds the capability of conventional current technology or methodology to process or analyze conventionally. To help meet the challenges, a multi-disciplinary area of data science has been tapped to pick up where conventional methods leave off. While the legal profession is no stranger to big data or science, this is the first formal effort by the ABA to establish global standards and best practices to fully meet the challenges imposed by big data using science.

The committee consists of the best in data science and law. We will continue to define the roadmap and provide other guidance for the pursuit of viable solutions for this committee and to further define its mission.

November 2014 Hot Topics presentation:
Jakob Halskov presented a group proposal on the promise of the new inter-disciplinary field called Behavior Informatics. Behavior Informatics is basically the combination of Behavior Sciences (Sociology, Psychology, Criminology etc.) with Informatics, producing tools ranging from Social Network Analysis to the automatic detection of anomalous behavioral patterns in Big Data. The book will cover three actual use cases, Audit and Compliance (FCPA), Investigation (Cartel), and Litigation (eDiscovery/IP infringement), as well as an appendix outlining the most relevant tools/technologies and their utility in each use case.


Data Scientists: data scientists are needed to cover the full spectrum of a data-science project lifecycle and from areas covering all verticals within data science to include structured and unstructured data, high-speed (high “velocity”) analytics, in-database applications, etc. Essentially, verticals that will need to be represented include Statistics (descriptive and inferential), Data Analysis, Database Engineering, Computer Programming, Linguistics / Natural Language Processing, Data Mining / Text Mining, Machine Learning and Information Retrieval. It is likely that one Data Scientist will be able to address more than one of these verticals. Data Scientists need not have a legal background but this is desirable.

Legal Professionals: attorneys, paralegals, litigation support, investigators and similar professionals with strong technical background who can assist with addressing legal principles in the application of data-science process, ensure cross-functional integrity of process and assist with best practices for use of data science in the legal profession. This is the first time this effort is known to have occurred so requirements for this area remain undefined in many areas.

To compound this, often, professionals from one data-science vertical are not familiar with what others do. This is one more reason a common model is needed – to keep all professionals across the data-science verticals focused in the same direction.

The Big Data Committee is currently working on a book series. The books will include educational material, best practices, practical advice, where to find useful resources, etc. The outlines are currently being refined but will include topics organized by practice area where use cases will provide hypothetical scenarios and where data-science solutions will be utilized. It is expected that most major legal-profession domain areas will be represented and major areas of data science will be discussed.

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Starrett, Paul

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Modified by Julia Passamani on May 16, 2016

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