Governance at the Edge of Architecture: Regulating NeuroAI and Neuromorphic Systems
This addresses governance challenges for emerging NeuroAI technologies, though it is incremental in proposing adaptations rather than new paradigms.
The paper examines how current AI governance frameworks fail to address NeuroAI systems like neuromorphic hardware and spiking neural networks, arguing that regulatory metrics must align with brain-inspired computation for effective assurance.
Current AI governance frameworks, including regulatory benchmarks for accuracy, latency, and energy efficiency, are built for static, centrally trained artificial neural networks on von Neumann hardware. NeuroAI systems, embodied in neuromorphic hardware and implemented via spiking neural networks, break these assumptions. This paper examines the limitations of current AI governance frameworks for NeuroAI, arguing that assurance and audit methods must co-evolve with these architectures, aligning traditional regulatory metrics with the physics, learning dynamics, and embodied efficiency of brain-inspired computation to enable technically grounded assurance.