@inproceedings{f155ba039cc74214bcfa548e9048af75,
title = "Automated Detection and Analysis of Data Practices Using A Real-World Corpus",
abstract = "Privacy policies are crucial for informing users about data practices, yet their length and complexity often deter users from reading them. In this paper, we propose an automated approach to identify and visualize data practices within privacy policies at different levels of detail. Leveraging crowd-sourced annotations from the ToS;DR platform, we experiment with various methods to match policy excerpts with predefined data practice descriptions. We further conduct a case study to evaluate our approach on a real-world policy, demonstrating its effectiveness in simplifying complex policies. Experiments show that our approach accurately matches data practice descriptions with policy excerpts, facilitating the presentation of simplified privacy information to users.",
author = "Mukund Srinath and Pranav Venkit and Maria Badillo and Florian Schaub and Giles, {C. Lee} and Shomir Wilson",
note = "Publisher Copyright: {\textcopyright} 2024 Association for Computational Linguistics.; Findings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024 ; Conference date: 11-08-2024 Through 16-08-2024",
year = "2024",
language = "English (US)",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "4567--4574",
editor = "Lun-Wei Ku and Andre Martins and Vivek Srikumar",
booktitle = "62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024 - Proceedings of the Conference",
}