Book ; Online: Automating Governing Knowledge Commons and Contextual Integrity (GKC-CI) Privacy Policy Annotations with Large Language Models
2023
Abstract: Identifying contextual integrity (CI) and governing knowledge commons (GKC) parameters in privacy policy texts can facilitate normative privacy analysis. However, GKC-CI annotation has heretofore required manual or crowdsourced effort. This paper ... ...
Abstract | Identifying contextual integrity (CI) and governing knowledge commons (GKC) parameters in privacy policy texts can facilitate normative privacy analysis. However, GKC-CI annotation has heretofore required manual or crowdsourced effort. This paper demonstrates that high-accuracy GKC-CI parameter annotation of privacy policies can be performed automatically using large language models. We fine-tune 18 open-source and proprietary models on 21,588 GKC-CI annotations from 16 ground truth privacy policies. Our best-performing model (fine-tuned GPT-3.5 Turbo with prompt engineering) has an accuracy of 86%, exceeding the performance of prior crowdsourcing approaches despite the complexity of privacy policy texts and the nuance of the GKC-CI annotation task. We apply our best-performing model to privacy policies from 164 popular online services, demonstrating the effectiveness of scaling GKC-CI annotation for data exploration. We make all annotated policies as well as the training data and scripts needed to fine-tune our best-performing model publicly available for future research. Comment: 24 pages, 7 figures, 10 tables |
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Keywords | Computer Science - Computers and Society ; Computer Science - Computation and Language ; Computer Science - Machine Learning |
Subject code | 303 |
Publishing date | 2023-11-03 |
Publishing country | us |
Document type | Book ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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