AI System Design and Governance
Summary
AI System Design and Governance is a critical area of research that focuses on developing scalable and secure methods for managing large-scale artificial intelligence systems. One promising approach in this field is Shared Model Governance through model splitting, which offers a more practical alternative to computationally intensive techniques like homomorphic encryption and secure multiparty computation. This method involves distributing a deep learning model among multiple parties and evaluates security through the “model completion problem.” Research in this area has revealed that the difficulty of reconstructing a model’s original performance depends more on the type and location of missing parameters than their quantity, with reinforcement learning scenarios presenting greater challenges due to the absence of trained agent trajectories. These findings suggest that model splitting could be an effective strategy for shared model governance, particularly in resource-intensive training scenarios, potentially revolutionizing how AI systems are designed and governed in the future.