From prescription to description: Mapping the GDPR to a privacy policy corpus annotation scheme

Ellen Poplavska, Thomas B. Norton, Shomir Wilson, Norman Sadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The European Union's General Data Protection Regulation (GDPR) has compelled businesses and other organizations to update their privacy policies to state specific information about their data practices. Simultaneously, researchers in natural language processing (NLP) have developed corpora and annotation schemes for extracting salient information from privacy policies, often independently of specific laws. To connect existing NLP research on privacy policies with the GDPR, we introduce a mapping from GDPR provisions to the OPP-115 annotation scheme, which serves as the basis for a growing number of projects to automatically classify privacy policy text. We show that assumptions made in the annotation scheme about the essential topics for a privacy policy reflect many of the same topics that the GDPR requires in these documents. This suggests that OPP-115 continues to be representative of the anatomy of a legally compliant privacy policy, and that the legal assumptions behind it represent the elements of data processing that ought to be disclosed within a policy for transparency. The correspondences we show between OPP-115 and the GDPR suggest the feasibility of bridging existing computational and legal research on privacy policies, benefiting both areas.

Original languageEnglish (US)
Title of host publicationLegal Knowledge and Information Systems - JURIX 2020
Subtitle of host publication33rd Annual Conference
EditorsSerena Villata, Jakub Harasta, Petr Kremen
PublisherIOS Press BV
Pages243-246
Number of pages4
ISBN (Electronic)9781643681504
DOIs
StatePublished - Dec 1 2020
Event33rd International Conference on Legal Knowledge and Information Systems, JURIX 2020 - Virtual, Online, Czech Republic
Duration: Dec 9 2020Dec 11 2020

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume334
ISSN (Print)0922-6389

Conference

Conference33rd International Conference on Legal Knowledge and Information Systems, JURIX 2020
CountryCzech Republic
CityVirtual, Online
Period12/9/2012/11/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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