Toward a Standards Framework for Hybrid Intelligence Governance: Integrating Human Judgment and AI Decision Support

Abstract

The rapid integration of artificial intelligence into private and public-sector decision-making has outpaced the development of standards governing the interaction between human judgment and machine intelligence. Existing frameworks—the EU AI Act Regulation, the NIST AI Risk Management Framework, and ISO/IEC 42001—regulate AI systems as discrete technical artifacts but do not standardize the hybrid intelligence configurations in which human cognition and algorithmic outputs jointly produce governance decisions. This paper proposes a three-layer standards framework comprising technical interoperability standards governing how AI outputs are communicated to human decision-makers, procedural standards governing human-AI task allocation and escalation protocols, and accountability standards governing responsibility attribution in distributed decision configurations. The framework is grounded in the Quadruple Bottom Line (QBL), which adds governance as a fourth sustainability dimension. To move beyond a purely conceptual contribution, the paper provides operationalization tools—including a role allocation matrix, confidence calibration thresholds, an accountability mapping template, and a domain classification schema—and proposes a three-tier conformity assessment methodology for evaluating framework implementation. By establishing the hybrid human–AI decision configuration as the unit of standardization, the paper introduces a governance architecture that enables operational, auditable, and comparable hybrid intelligence systems.