  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.
