Human-in-the-Loop Reinforcement Learning for AI Governance: A Fast–Slow Decision Paradigm for Responsible LLM Deployment
Keywords:
human-in-the-loop reinforcement learning, AI governance, fast–slow decision paradigm, large language models, responsible deployment, dual-process theory, reward design, safety alignmentAbstract
The rapid deployment of large language models (LLMs) in high-stakes domains such as healthcare, finance, and legal reasoning has intensified concerns regarding their alignment with human values, fairness, and long-term safety. Traditional reinforcement learning (RL) approaches for LLM alignment, including reinforcement learning from human feedback (RLHF), rely on a static reward model and a single loop of human annotation, which fail to adapt to evolving societal norms and context-sensitive ethical dilemmas. This paper proposes a novel governance framework that integrates human-in-the-loop reinforcement learning with a fast–slow decision paradigm inspired by dual-process cognitive theory. The framework distinguishes between fast, automatic LLM responses that are optimized for efficiency and slow, deliberative interventions that involve human oversight and metacognitive reasoning. We introduce a human-in-the-loop RL architecture where a supervisory human agent dynamically adjusts the balance between fast and slow pathways based on risk estimation, uncertainty quantification, and policy compliance. This architecture is implemented through a hierarchical reward structure that couples immediate performance rewards with long-term governance penalties. We analyze structural trade-offs between system responsiveness and regulatory robustness, and discuss deployment considerations including scalability, auditability, and resilience to adversarial manipulation. Cross-domain comparisons with autonomous driving and algorithmic trading illustrate the generality of the paradigm. We conclude by outlining policy implications for responsible LLM deployment and proposing a governance lifecycle that integrates continuous human oversight with adaptive RL mechanisms.
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