Researchers are increasingly focused on the challenges of governing NeuroAI and neuromorphic systems, a field where current regulatory approaches fall short. Afifah Kashif from the University of Cambridge, Abdul Muhsin Hameed from the University of Washington, and Asim Iqbal from Cornell University, et al., demonstrate that existing governance frameworks, designed for static artificial neural networks running on conventional hardware, are inadequate for these fundamentally different architectures. This paper highlights a critical need to reassess assurance and audit methods, advocating for a co-evolution of regulation alongside brain-inspired computation to ensure technically sound and effective oversight of NeuroAI’s unique physics, learning dynamics, and efficiency. Understanding these limitations and proposing adaptive governance is significant as NeuroAI promises substantial advancements in energy efficiency and real-time processing, but requires careful management to realise its potential safely and responsibly.
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