Defining Operational Conditions for Safety-Critical AI-Based Systems from Data
This addresses the problem of incomplete environmental descriptions for certification in safety-critical domains like aviation, though it appears incremental as it builds on existing ODD concepts with a new data-driven approach.
The paper tackles the challenge of defining Operational Design Domains (ODDs) for safety-critical AI systems by proposing a data-driven method using multi-dimensional kernel-based representations, validated with Monte Carlo simulations and a real-world aviation case, showing that the data-driven ODD can equal the hidden underlying ODD.
Artificial Intelligence (AI) has been on the rise in many domains, including numerous safety-critical applications. However, for complex systems found in the real world, or when data already exist, defining the underlying environmental conditions is extremely challenging. This often results in an incomplete description of the environment in which the AI-based system must operate. Nevertheless, this description, called the Operational Design Domain (ODD), is required in many domains for the certification of AI-based systems. Traditionally, the ODD is created in the early stages of the development process, drawing on sophisticated expert knowledge and related standards. This paper presents a novel Safety-by-Design method to a posteriori define the ODD from previously collected data using a multi-dimensional kernel-based representation. This approach is validated through both Monte Carlo methods and a real-world aviation use case for a future safety-critical collision-avoidance system. Moreover, by defining under what conditions two ODDs are equal, the paper shows that the data-driven ODD can equal the original, underlying hidden ODD of the data. Utilizing the novel, Safe-by-Design kernel-based ODD enables future certification of data-driven, safety-critical AI-based systems.