Explaining the unexplainable: data sharing and privacy in Web 3.0

Abstract

Web 3.0 transforms data ownership by empowering users to monetize their personal information, yet this shift amplifies complex privacy challenges. Understanding the features influencing data-sharing decisions in this evolving context aligns with the proactive philosophy of designing legal frameworks that anticipate and mitigate conflicts. This study leverages explainable AI (XAI) to analyze user privacy behaviors and other privacy attributes, using data from 682 individuals in Korea within a personalized app scenario. The findings reveal a privacy paradox: users with low perceived data value but high privacy concerns often share their data, whereas those with higher trust in services are more hesitant, prioritizing long-term privacy and service continuity. These insights demonstrate that a one-size-fits-all legal or regulatory approach cannot adequately address the nuances of Web 3.0 data-sharing dynamics. Instead, this study advocates for a proactive law framework that integrates user research and XAI insights to anticipate conflicts and opportunities. By understanding individual intentions and concerns, proactive law can deliver tailored, context-sensitive solutions that balance privacy, user autonomy and economic opportunity, ensuring equitable and sustainable outcomes in the decentralized Web 3.0 ecosystem.