A Solution toward Transparent and Practical AI Regulation: Privacy Nutrition Labels for Open-source Generative AI-based Applications
The rapid development and widespread adoption of Generative Artificial Intelligence-based (GAI) applications have greatly enriched our daily lives, benefiting people by enhancing creativity, personalizing experiences, improving accessibility, and fostering innovation and efficiency across various domains. However, along with the development of GAI applications, concerns have been raised about transparency in their privacy practices. Traditional privacy policies often fail to effectively communicate essential privacy information due to their complexity and length, and open-source community developers often neglect privacy practices even more.
Resource information
- Authors
- Si, Meixue, Pan, Shidong, Liao, Dianshu, Sun, Xiaoyu, Tao, Zhen, Shi, Wenchang, Xing, Zhenchang
- Research area
- Artificial intelligence
- Research topics
- Cryptography and Security (cs.CR), Software Engineering (cs.SE), FOS: Computer and information sciences, FOS: Computer and information sciences
- Publication date
- 2024
- DOI
- 10.48550/arxiv.2407.15407
- Upstream last modified
- Jul 22, 2024
- License
- Creative Commons Attribution 4.0 International
- Last record update
- Nov 5, 2026
About this resource
The rapid development and widespread adoption of Generative Artificial Intelligence-based (GAI) applications have greatly enriched our daily lives, benefiting people by enhancing creativity, personalizing experiences, improving accessibility, and fostering innovation and efficiency across various domains. However, along with the development of GAI applications, concerns have been raised about transparency in their privacy practices. Traditional privacy policies often fail to effectively communicate essential privacy information due to their complexity and length, and open-source community developers often neglect privacy practices even more. Only 12.2% of examined open-source GAI apps provide a privacy policy. To address this, we propose a regulation-driven GAI Privacy Label and introduce Repo2Label, a novel framework for automatically generating these labels based on code repositories. Our user study indicates a common endorsement of the proposed GAI privacy label format. Additionally, Repo2Label achieves a precision of 0.81, recall of 0.88, and F1-score of 0.84 based on the benchmark dataset, significantly outperforming the developer self-declared privacy notices. We also discuss the common regulatory (in)compliance of open-source GAI apps, comparison with other privacy notices, and broader impacts to different stakeholders. Our findings suggest that Repo2Label could serve as a significant tool for bolstering the privacy transparency of GAI apps and make them more practical and responsible.