Safety of AI Systems with Executable Causal Models and Statistical Data Science

AI systems that learn from data present a unique challenge for safety, as there is no specific design artifact, model, or code to analyse and verify. The safety assurance challenges become even more complex in cooperative intelligent systems, like collaborative robots and autonomous vehicles. These systems are often loosely interconnected, allowing them to form and dissolve configurations dynamically. Evaluating the consequences of failures in largely unpredictable configurations is a daunting task. Intentional or unintentional interactions between systems, along with newly learned behaviours and varying environmental conditions, can lead to unpredictable or emergent behaviours. Achieving complete safety assurance of such systems of systems at the design stage through traditional model-based methods is unfeasible. In this talk, I will explore these challenges and introduce executable causal models and statistical techniques that may help address these emerging issues.