Privacy Policy
Privacy policies, crucial for informing users about data handling practices and ensuring regulatory compliance, are the subject of intense research focusing on improving their clarity, accessibility, and automated analysis. Current efforts leverage techniques like natural language processing, large language models, and logic-based representations to enhance policy comprehension and compliance audits, while also exploring the interplay between privacy, fairness, and utility in machine learning models. This research is vital for promoting transparency and accountability in data practices, impacting both regulatory oversight and the development of trustworthy AI systems.
Papers
Are Chatbots Ready for Privacy-Sensitive Applications? An Investigation into Input Regurgitation and Prompt-Induced Sanitization
Aman Priyanshu, Supriti Vijay, Ayush Kumar, Rakshit Naidu, Fatemehsadat Mireshghallah
A Human-in-the-Loop Approach for Information Extraction from Privacy Policies under Data Scarcity
Michael Gebauer, Faraz Maschhur, Nicola Leschke, Elias Grünewald, Frank Pallas
Trade-Offs Between Fairness and Privacy in Language Modeling
Cleo Matzken, Steffen Eger, Ivan Habernal
Can Copyright be Reduced to Privacy?
Niva Elkin-Koren, Uri Hacohen, Roi Livni, Shay Moran
ATLAS: Automatically Detecting Discrepancies Between Privacy Policies and Privacy Labels
Akshath Jain, David Rodriguez, Jose M. del Alamo, Norman Sadeh