Formalising Functional Responsibility in Hybrid Multi-agent Systems

We present a multi-modal Logic for Functional Responsibility (LFR) to model and check actual, capacity, and outcome responsibility of agents within hybrid computational systems with a user level ontology. For AI systems, this allows one to represent and trace responsibility of output to training data, training engine, trained model, policies and end user operations. We exemplify the system with an example of image generation.