@inproceedings{srinath-etal-2024-automated,
title = "Automated Detection and Analysis of Data Practices Using A Real-World Corpus",
author = "Srinath, Mukund and
Narayanan Venkit, Pranav and
Badillo, Maria and
Schaub, Florian and
Giles, C. and
Wilson, Shomir",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.271/",
doi = "10.18653/v1/2024.findings-acl.271",
pages = "4567--4574",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Automated Detection and Analysis of Data Practices Using A Real-World Corpus
%A Srinath, Mukund
%A Narayanan Venkit, Pranav
%A Badillo, Maria
%A Schaub, Florian
%A Giles, C.
%A Wilson, Shomir
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F srinath-etal-2024-automated
%X 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.
%R 10.18653/v1/2024.findings-acl.271
%U https://aclanthology.org/2024.findings-acl.271/
%U https://doi.org/10.18653/v1/2024.findings-acl.271
%P 4567-4574
Markdown (Informal)
[Automated Detection and Analysis of Data Practices Using A Real-World Corpus](https://aclanthology.org/2024.findings-acl.271/) (Srinath et al., Findings 2024)
ACL