Ensuring Fairness in Artificial General Intelligence: A Review of Real-Time Ethical Monitoring Frameworks

Artificial General Intelligence (AGI) is advancing toward human-like cognition, yet ensuring ethical decisionmaking remains a significant challenge due to bias, accountability concerns, and the lack of real-time auditing mechanisms. This research proposes a real-time ethical monitoring framework that integrates explainable AI (XAI), reinforcement learning-based ethical modeling, and adversarial robustness techniques to enhance transparency, fairness, and accountability in AGI-driven decision-making. Utilizing graph neural networks (GNNs), t-SNE embeddings, and federated learning, the framework continuously audits AGI models across diverse high-risk domains such as finance and healthcare. Experimental results show a 29% improvement in ethical compliance, a 23% reduction in bias, and a 35% increase in interpretability compared to baseline AI systems. The adaptive self-learning mechanism ensures real-time anomaly detection without significant computational overhead. This study sets a new benchmark for scalable, real-time AGI auditing, paving the way for responsible AI governance. Future work will explore quantum AI integration and decentralized AGI monitoring using blockchain for enhanced security and trustworthiness.