Media Party New York

Imagine using software to transcribe your interview into text, unaware that the privacy policy you agreed to allows the company to use your unfinished work to train their AI model. This could be alarming for an investigative journalist! In this lightning talk, I will share findings from privacy policies of commonly used journalism tools, including those for online conferences, transcribing, social media, and more. I'll highlight which tools are safe and which ones raise concerns based on a thorough analysis of their privacy policies, so you don’t have to sift through them yourself. These findings are part of our PrivaSee project, which won a grant from the Columbia Venture Challenge. We analyze the latest privacy policies from app stores, identifying patterns in privacy and AI. We want to use this project to first protect journalists' privacy and share our findings with you. Here’s the structure of the talk: 1. The Problem: Companies are leveraging user data to train their AI models, embedding this practice within their privacy policies. 2. Why It Matters: Investigative journalists need to protect their unpublished work, and newsrooms often sell content to large companies for revenue. 3. Key Findings: An overview of commonly used tools for journalists, identifying which are safe and which may pose risks. 4. Solutions: The best strategies we've identified for keeping your content safe while working efficiently. 5. Methodology and Limitations: How we conducted this project and how it will continuously monitor and update privacy policies. 6. Project and Acknowledgments: An introduction to our PrivaSee project, a simple demonstration of the prototype, and thanks to the Brown Institute for Media Innovation.