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.
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).