Much Ado About Compute- Challenging the reliance on training FLOPs as a proxy for Safety in AI Regulation

This paper was awarded First Prize (Excellence in Research and shows that these justifications, while partially valid, fail to address the growing ease with which AI capability can be scaled, obfuscated, or repurposed outside the scope of compute-based AI regulatory oversight. To address these realities, the paper proposes a detailed Capability-Assessment based AI Safety Framework (CAP-SAFE Framework) that takes into consideration dynamic capability-based regulatory triggers, actual inference capabilities, deployability metrics, deployment context etc. alongside training compute to evaluate models for AI Safety, with specific policy pillars and recommendations for the same. The CAP-SAFE Framework aims to prioritize metrics that predict dysregulated AI proliferation and misuse to ensure that AI safety governance remains agile and resilient in an era where emergent AI risk potential no longer correlates neatly with high AI training compute.