# Automatic Detection of Vague Words and Sentences in Privacy Policies

arXiv: https://arxiv.org/abs/1808.06219 · HTML: https://mlsanity.com/paper/1808.06219/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2018.

## Built on by (most cited first, 10 of 10)

- [RT-LM: Uncertainty-Aware Resource Management for Real-Time Inference of Language Models](https://mlsanity.com/paper/2309.06619/) (2023)
- [Measuring vagueness and subjectivity in texts: from symbolic to neural VAGO](https://mlsanity.com/paper/2309.06132/) (2023)
- [FEED PETs: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms](https://mlsanity.com/paper/2306.00217/) (2023)
- [Towards Mitigating Perceived Unfairness in Contracts from a Non-Legal Stakeholder's Perspective](https://mlsanity.com/paper/2312.01398/) (2023)
- [Generating Clarification Questions for Disambiguating Contracts](https://mlsanity.com/paper/2403.08053/) (2024)
- [A Computational Analysis of Vagueness in Revisions of Instructional Texts](https://mlsanity.com/paper/2309.12107/) (2023)
- [Automating Conflict-Aware ACL Configurations with Natural Language Intents](https://mlsanity.com/paper/2508.17990/) (2025)
- [Enabling Multilingual Privacy Policy Audits: Large-Scale Analysis of Spanish Mobile Apps](https://mlsanity.com/paper/2607.18424/) (2026)
- [Privacy Washing: Detecting Internal Contradictions in Privacy Policies](https://mlsanity.com/paper/2609.02055/) (2026)
- [Fake News Theories: Harnessing Disciplinary Insights for Computational Modeling, Detection, and Explanation](https://mlsanity.com/paper/2609.30427/) (2026)

## Abstract (arXiv)

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.