A Human-in-the-Loop Approach for Information Extraction from Privacy Policies under Data Scarcity

Understand

Machine-readable representations of privacy policies are door openers for a broad variety of novel privacy-enhancing and, in particular, transparency-enhancing technologies (TETs).

• In order to generate such representations, transparency information needs to be extracted from written privacy policies.
• However, respective manual annotation and extraction processes are laborious and require expert knowledge.
• Approaches for fully automated annotation, in turn, have so far not succeeded due to overly high error rates in the specific domain of privacy policies.