(untitled) (Journal of Generative Intelligence E: 3117-6429 P: 3117-6437, 2025)
Human-in-the-loop (HITL) systems – architectures that maintain meaningful human oversight and intervention capability in AI-assisted processes – are a central governance requirement for high-stakes generative AI deployment under the EU AI Act (2024) and a fundamental principle of responsible AI frameworks. However, the practical design of effective HITL architectures for generative AI presents distinct challenges compared to HITL systems for classification-based AI: the open-ended output space makes exhaustive human review infeasible at scale; the fluency and confidence of generative outputs can undermine human critical evaluation; and the high throughput requirements of deployed generative systems create time pressure that degrades human review quality. This paper proposes the Generative AI Human Oversight (GAHO) framework, a systematic methodology for designing, implementing, and evaluating HITL systems for generative AI deployment across four oversight models: full review (FR), risk-stratified review (RSR), spot-check audit (SCA), and algorithmic-human collaborative review (AHCR). GAHO integrates a risk stratification engine (RSE) that routes outputs to appropriate oversight levels based on uncertainty, context, and domain risk; a human reviewer support system (HRSS) that provides reviewers with GEF explainability, uncertainty estimates, and bias indicators; and a review quality monitoring system (RQMS) that tracks reviewer performance and flags review degradation. GAHO is evaluated through a controlled deployment study across three high-stakes generative AI applications: clinical note generation, legal document drafting, and financial report generation, with 128 human reviewers over 60 days. GAHO RSR achieves 94.2% harmful output detection rate (SD = 3.1%) at 42.8% review cost reduction relative to full review, while AHCR achieves 96.8% detection at 61.4% cost reduction. Reviewer support tools (HRSS) improve detection accuracy by 18.4pp over unsupported review. The study contributes the GAHO specification, a Human Oversight Effectiveness (HOE) metric, and empirical design guidelines for responsible generative AI deployment.