Automatic Detection of Vague Words and Sentences in Privacy Policies
This addresses the challenge for users and regulators in understanding opaque privacy policies, though it is incremental in applying existing NLP methods to a new domain.
The paper tackles the problem of vague content in privacy policies by constructing the first human-annotated corpus and developing automatic detection models, achieving effective results as demonstrated in experiments.
Website privacy policies represent the single most important source of information for users to gauge how their personal data are collected, used and shared by companies. However, privacy policies are often vague and people struggle to understand the content. Their opaqueness poses a significant challenge to both users and policy regulators. In this paper, we seek to identify vague content in privacy policies. We construct the first corpus of human-annotated vague words and sentences and present empirical studies on automatic vagueness detection. In particular, we investigate context-aware and context-agnostic models for predicting vague words, and explore auxiliary-classifier generative adversarial networks for characterizing sentence vagueness. Our experimental results demonstrate the effectiveness of proposed approaches. Finally, we provide suggestions for resolving vagueness and improving the usability of privacy policies.