Rhodes, Greece. September 2-8, 2023.
ISSN: 2334-1033
ISBN: 978-1-956792-02-7
Copyright © 2023 International Joint Conferences on Artificial Intelligence Organization
The need for tools and techniques to formally analyze and trace the responsibility for unsafe outcomes to decision-making actors is urgent. Existing formal approaches assume that the unsafe outcomes for which actors can be held responsible are actually realized. This paper considers a broader notion of responsibility where unsafe outcomes are not necessarily realized, but their probabilities are unacceptably high. We present a logic combining strategic, probabilistic and temporal primitives designed to express concepts such as the risk of an undesirable outcome and being responsible for exceeding a risk threshold. We demonstrate that the proposed logic is complete and decidable.