Control Tax: The Price of Keeping AI in Check
This work addresses the practical adoption of AI control mechanisms for high-stakes applications by assessing economic feasibility, though it is incremental in advancing the field of AI Control.
The paper tackles the problem of high costs for implementing AI control measures by introducing Control Tax, a framework quantifying operational and financial costs, and provides empirical cost estimates and optimized monitoring strategies, achieving a balance between safety and cost-effectiveness with specific financial estimates.
The rapid integration of agentic AI into high-stakes real-world applications requires robust oversight mechanisms. The emerging field of AI Control (AIC) aims to provide such an oversight mechanism, but practical adoption depends heavily on implementation overhead. To study this problem better, we introduce the notion of Control tax -- the operational and financial cost of integrating control measures into AI pipelines. Our work makes three key contributions to the field of AIC: (1) we introduce a theoretical framework that quantifies the Control Tax and maps classifier performance to safety assurances; (2) we conduct comprehensive evaluations of state-of-the-art language models in adversarial settings, where attacker models insert subtle backdoors into code while monitoring models attempt to detect these vulnerabilities; and (3) we provide empirical financial cost estimates for control protocols and develop optimized monitoring strategies that balance safety and cost-effectiveness while accounting for practical constraints like auditing budgets. Our framework enables practitioners to make informed decisions by systematically connecting safety guarantees with their costs, advancing AIC through principled economic feasibility assessment across different deployment contexts.