AAAI Publications, Second AAAI Conference on Human Computation and Crowdsourcing

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Identifying Relevant Text Fragments to Help Crowdsource Privacy Policy Annotations
Rohan Ramanath, Florian Schaub, Shomir Wilson, Fei Liu, Norman Sadeh, Noah A Smith

Last modified: 2014-09-05

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.

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