In multi-agent systems, emergent norms and distributed decision-making often produce unanticipated behaviors that complicate traditional AI governance frameworks. This paper introduces an adaptive accountability method that traces responsibility flows among networked agents, continuously detects adverse emergent norms, and intervenes to recalibrate local objectives or policies in near real time. By combining lifecycle-based auditing, decentralized governance, and norm detection algorithms, our approach enables robust oversight in dynamic, evolving environments. To validate its scalability and effectiveness, we conduct a series of large-scale simulation experiments on up to 100 agents using an HPC environment. Our ablation studies—covering multiple seeds, varied penalty settings, and different intervention policies—demonstrate that the framework can preserve high collective reward while significantly reducing inequality. In particular, we show that adaptive interventions prevent harmful collusion or hoarding in over 90% of tested configurations, even under partial observability. These results indicate that our method not only mitigates unforeseen disruptions but also aligns agent behaviors with ethical and legal guidelines at scale. Overall, the resulting framework offers a practical path toward ethically sound, multi-agent AI systems that remain responsive to shifting data distributions, organizational policies, and real-world complexity.
Saad Alqithami (Wed,) studied this question.