Extending Causal Models from Machines into Humans

Severin Kacianka
(TU Munich)
Amjad Ibrahim
(TU Munich)
Alexander Pretschner
(TU Munich)
Alexander Trende
(Offis)
Andreas Lüdtke
(Offis)

Causal Models are increasingly suggested as a means to reason about the behavior of cyber-physical systems in socio-technical contexts. They allow us to analyze courses of events and reason about possible alternatives. Until now, however, such reasoning is confined to the technical domain and limited to single systems or at most groups of systems. The humans that are an integral part of any such socio-technical system are usually ignored or dealt with by "expert judgment". We show how a technical causal model can be extended with models of human behavior to cover the complexity and interplay between humans and technical systems. This integrated socio-technical causal model can then be used to reason not only about actions and decisions taken by the machine, but also about those taken by humans interacting with the system. In this paper we demonstrate the feasibility of merging causal models about machines with causal models about humans and illustrate the usefulness of this approach with a highly automated vehicle example.

In Georgiana Caltais and Jean Krivine: Proceedings of the 4th Workshop on Formal Reasoning about Causation, Responsibility, and Explanations in Science and Technology (CREST 2019), Prague, Czech Republic, 7th April 2019, Electronic Proceedings in Theoretical Computer Science 308, pp. 17–31.
Published: 31st October 2019.

ArXived at: http://dx.doi.org/10.4204/EPTCS.308.2 bibtex PDF
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