This Project examines a range of automated techniques to assess compliance with GDPR (General Data Protection Regulation) through analysis of all relevant data and information pertaining to a company (or a department within a company or another legal entity).
This project will develop the processes to allow ventures, large or small, to sort through its documents and databases to determine whether the company will be in compliance with the data usage, privacy laws and privacy practices specified by the GDPR.
News/Updates
This project will lead students through the development of a workflow process to assist startup entrepreneurs and small businesses identifying potential GDPR compliance risk areas. Students will convert the workflow into a structured, interactive chat dialogue that will interrogate users following the designed workflow to flag risk areas.
RAVN, after being acquired by iManage, has shifted its focus from GDPR to enterprise-level legal assistance tools rooted in their machine learning technology. As a result, RAVN/iManage is no longer a suitable partner for this project since the inclusion of a parent company with different objectives has resulted in a realignment of RAVN’s priorities. Brooklyn Law School’s Brooklyn Law Incubator and Policy (BLIP) Clinic will explore potential partnerships with other firms offering AI/Machine Learning.
The skills and processes learned in the context of European privacy laws will then be applied to automate and apply machine learning techniques to other areas of legal concern (e.g., understanding and extracting data from a broad array of legal documents; parsing through and reconciling potentially conflicting open source and other licensing schemes).
News/Updates
The students and faculty at Brooklyn Law (through support from the Legal Technology Lab) initially created a scripted dialogue interface to help small businesses and entrepreneurs better understand the GDPR, and to figure out when they might run afoul of the GDPR (see learning tool at https://potkewitz.github.io/QnA/GDPR_Learner.html and letter tool at https://potkewitz.github.io/QnA/GDPR_Letter.html). The knowledge gained from building the rudimentary GDPR tool has subsequently been applied by more recent student teams (through a “Justice Lab”) to build chatbots, apps, sites scripting laws, regulations, and policies to help different constituencies, mostly vulnerable, marginalized communities and individuals without ready access to legal support to better understand their rights and responsibilities. Among the tools that Brooklyn Law students have built since the LTL GDPR project are projects designed to help vulnerable individuals with family law matters, workers’ rights issues (notably restaurant workers and sex workers), and access to government services.
GDPR QnA
QnA markup (http://www.qnamarkup.org/) is a simple, open source markup language designed to assist people without coding experience to generate structured chat dialogs that present in a fashion similar to text message chats. The QnA site teaches users the rules of its simplified syntax and allows users to enter their QnA scripts in a text field on the page and render them for testing and debugging.
BLIP LTL GDPR Learning Tool (unrendered QnA scripts)145.98 KB