Detecting Compute Structuring in AI Governance is likely feasible

Emmanouil Seferis, Timothy Fist

Keywords: Compute Structuring, AI Governance

TL;DR: We argue that detecting compute structuring in AI governance is technically feasible

Abstract: Compute structuring, a technique where AI developers split or modify compute workloads for the purpose of avoiding regulation, poses a challenge for AI governance techniques that rely on the computational properties of AI workloads. This work aims to explore the feasibility of detecting compute structuring and to propose robust detection methods. We do this by first exploring possible forms of compute structuring. Using realistic assumptions about cloud providers’ capabilities, we derive a potential detection approach. Further, we perform a comprehensive analysis of possible adversary scenarios and show that our method can detect them efficiently. Finally, we analyze potential future trends in AI compute workloads that could invalidate our proposed detection approach, and discuss possible adaptation and mitigation strategies. Overall, our study indicates that compute structuring detection is probably both feasible and practical to implement.

Submission Number: 23