🤖 AI Summary
This paper addresses unique data governance challenges arising from artificial general intelligence (AGI) systems endowed with recursive self-improvement and self-replication capabilities. It identifies seven urgent, AGI-specific risks surpassing those of conventional AI: autonomous data acquisition bypassing informed consent; data retention decisions driven by optimization objectives rather than human values; supranational, unregulated data sharing among decentralized AGI agents; erosion of data provenance due to dynamic system evolution; ambiguous ownership of AI-generated content; diminished regulatory enforcement across jurisdictions; and rapid obsolescence of static governance frameworks. Employing conceptual analysis and systematic reasoning, the study integrates theories of recursive self-improvement, data provenance, and cross-border regulatory compliance to construct a risk identification and response framework. Its key contribution is a novel tripartite governance paradigm—“embedded safety constraints, real-time adaptive monitoring, and multilateral co-evolution”—advancing data governance from static rule-based models toward continuous, adaptive evolution, thereby offering theoretical foundations and actionable pathways for global AGI policy development.
📝 Abstract
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. We identify 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. We conclude 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.