2025/08/05 by Masayuki Hatta, Hatta, Masayuki · 1 voice · 1 citation
Computer Science · Decision Sciences · Medicine · Social Sciences · #Artificial Intelligence in Healthcare and Education #Computer science #Data science #Ethics and Social Impacts of AI #Scientific Computing and Data Management #cs.CY
paper · pdf · doi:10.1007/978-3-032-00686-8_22
openalex publication_date 2025/08/05 · arxiv published 2025/08/16 · arxiv updated 2025/08/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The rapid advancement of artificial intelligence has positioned data governance as a critical concern for responsible AI development. While frameworks exist for conventional AI systems, the potential emergence of Artificial General Intelligence (AGI) presents unprecedented governance challenges. This paper examines data governance challenges specific to AGI, defined as systems capable of recursive self-improvement or self-replication. This paper identifies seven key issues that differentiate AGI governance from current approaches. First, AGI may autonomously determine what data to collect and how to use it, potentially circumventing existing consent mechanisms. Second, these systems may make data retention decisions based on internal optimization criteria rather than human-established principles. Third, AGI-to-AGI data sharing could occur at speeds and complexities beyond human oversight. Fourth, recursive self-improvement creates unique provenance tracking challenges, as systems evolve both themselves and how they process data. Fifth, ownership of data and insights generated through self-improvement raises complex intellectual property questions. Sixth, self-replicating AGI distributed across jurisdictions would create unprecedented challenges for enforcing data protection laws. Finally, governance frameworks established during early AGI development may quickly become obsolete as systems evolve. This paper proposes concrete solutions including technical safeguards, policy frameworks, and governance mechanisms. This paper concludes that effective AGI data governance requires built-in constraints, continuous monitoring mechanisms, dynamic governance structures, international coordination, and multi-stakeholder involvement. Without forward-looking governance approaches specifically designed for systems with autonomous data capabilities, we risk creating AGI whose relationship with data evolves in ways that undermine human values and interests.