Combining Cost Constrained Runtime Monitors for AI Safety
Tim Hua, James Baskerville, Henri Lemoine, Mia Hopman, Aryan Bhatt, Tyler Tracy
Monitoring AIs at runtime could help us detect and stop harmful actions. However, both monitoring and safety interventions (e.g., human audits) incur costs. In this paper, we study how to combine multiple runtime monitors into a single monitoring protocol. The protocol's objective is to maximize the probability of applying a safety intervention on any misaligned outputs (i.e., maximize recall). Simultaneously, it must adhere to an average-case budget constraint over monitor calls and safety interventions. Taking monitors' performance and cost as given, we find the best protocol by exhaustively searching over when to call monitors and which monitors to call. Given a monitor invocation strategy, we allocate the safety intervention to the most suspicious outputs as measured by likelihood ratios. Finally, we pick the best monitor invocation strategy based on its recall rate and use it as our protocol. By focusing on likelihood ratios and strategically trading off spending on monitors and spending on interventions, we can more than double our recall rate compared to the naive baseline in a code review setting. We also show that combining two monitors can Pareto dominate using either monitor alone. Our framework provides a principled methodology for combining existing monitors to detect undesirable behavior in cost-sensitive settings.