Alessandro Abate

This paper introduces Guaranteed Safe (GS) AI, an approach to AI safety that ensures high-assurance quantitative safety guarantees. It relies on three core components—a world model, a safety specification, and a verifier—to mathematically verify that AI systems meet safety requirements.

STARC (STAndardised Reward Comparison) metrics, a class of pseudometrics, quantify differences between reward functions, providing theoretical and empirical tools to improve the analysis and safety of reward learning algorithms in reinforcement learning.