The paper 'AI + safety' asserts that data-driven models alone may not be sufficient to ensure safety and calling for a combination of data and causal models to mitigate risk. The position paper details the advance of AI and how such autonomous and self-learning systems are becoming more and more responsible for making safety-critical decisions. The operation of many safety-critical systems has traditionally been automated through control theory by making decisions based on a predefined set of rules and the current state of the system. Conversely, AI tries to automatically learn reasonable rules based on previous experience.
Since major incidents in the oil and gas industry are fortunately scarce, such scenarios are not well captured by data-driven models alone as not enough failure-data is available to make such critical decisions. AI and machine-learning algorithms, which currently rely on data-driven models to predict and act upon future scenarios, may not be sufficient then to assure safe operations and protect lives.
DNV seeks to combine the best of the traditional physics-based methods with the opportunities provided by novel data-driven approaches. The position paper stresses that if the industry can supplement these data-driven models by generating physics-based casual data, it will be significantly closer to the safe implementation of AI in safety-critical systems.