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.

Calcium imaging is an important technique for monitoring the activity of thousands of neurons simultaneously. As calcium imaging datasets grow in size, automated detection of individual neurons is becoming important. Here we apply a supervised learning approach to this problem and show that convolutional networks can achieve near-human accuracy and superhuman speed. Accuracy is superior to the popular PCA/ICA method based on precision and recall relative to ground truth annotation by a human expert. These results suggest that convolutional networks are an efficient and flexible tool for the analysis of large-scale calcium imaging data.

Calcium imaging is an important technique for monitoring the activity of thousands of neurons simultaneously. As calcium imaging datasets grow in size, automated detection of individual neurons is becoming important. Here we apply a supervised learning approach to this problem and show that convolutional networks can achieve near-human accuracy and superhuman speed. Accuracy is superior to the popular PCA/ICA method based on precision and recall relative to ground truth annotation by a human expert. These results suggest that convolutional networks are an efficient and flexible tool for the analysis of large-scale calcium imaging data.