AINeutralarXiv – CS AI · Apr 206/10
🧠
From Vulnerable Data Subjects to Vulnerabilizing Data Practices: Navigating the Protection Paradox in AI-Based Analyses of Platformized Lives
This academic paper examines how AI and data science practices can paradoxically increase vulnerability of subjects they aim to protect, using a case study of computer vision analysis of children in monetized YouTube content. The authors develop an ethics protocol identifying four critical decision points—dataset design, operationalization, inference, and dissemination—where technical choices create vulnerabilizing factors including exposure, monetization, narrative fixing, and algorithmic optimization.