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AI-Based Safety-Critical Systems (AI-SCS) are being increasingly deployed inthe real world. These can pose a risk of harm to people and the environment.Reducing that risk is an overarching priority during development and operation.As more AI-SCS become autonomous, a layer of risk management via humanintervention has been removed. Following an accident it will be important toidentify causal contributions and the different responsible actors behind thoseto learn from mistakes and prevent similar future events. Many authors havecommented on the "responsibility gap" where it is difficult for developers andmanufacturers to be held responsible for harmful behaviour of an AI-SCS. Thisis due to the complex development cycle for AI, uncertainty in AI performance,and dynamic operating environment. A human operator can become a "liabilitysink" absorbing blame for the consequences of AI-SCS outputs they weren'tresponsible for creating, and may not have understanding of.  This cross-disciplinary paper considers different senses of responsibility(role, moral, legal and causal), and how they apply in the context of AI-SCSsafety. We use a core concept (Actor(A) is responsible for Occurrence(O)) tocreate role responsibility models, producing a practical method to captureresponsibility relationships and provide clarity on the previously identifiedresponsibility issues. Our paper demonstrates the approach with two examples: aretrospective analysis of the Tempe Arizona fatal collision involving anautonomous vehicle, and a safety focused predictive role-responsibilityanalysis for an AI-based diabetes co-morbidity predictor. In both examples ourprimary focus is on safety, aiming to reduce unfair or disproportionate blamebeing placed on operators or developers. We present a discussion and avenuesfor future research.

What's my role? Modelling responsibility for AI-based safety-critical systems