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Identifying Relevant Text Fragments to Help Crowdsource Privacy Policy Annotations

Authors

Rohan Ramanath,Florian Schaub,Shomir Wilson,Fei Liu,Norman Sadeh,Noah Smith

Carnegie Mellon University,Carnegie Mellon University,Carnegie Mellon University,Carnegie Mellon University,Carnegie Mellon University,Carnegie Mellon University


Published:

2014-11-05

Proceedings:

Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2

Volume

Issue:

Vol. 2 (2014): Second AAAI Conference on Human Computation and Crowdsourcing

Track:

Works in Progress

Downloads:

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Abstract:

In today's age of big data, websites are collecting an increasingly wide variety of information about their users. The texts of websites' privacy policies, which serve as legal agreements between service providers and users, are often long and difficult to understand. Automated analysis of those texts has the potential to help users better understand the implications of agreeing to such policies. In this work, we present a technique that combines machine learning and crowdsourcing to semi-automatically extract key aspects of website privacy policies that is scalable, fast, and cost-effective.

DOI:

10.1609/hcomp.v2i1.13179


HCOMP

Vol. 2 (2014): Second AAAI Conference on Human Computation and Crowdsourcing


ISBN 978-1-57735-682-0