Biblio

The increasing use of automated decision making (ADM) and machine learning sparked an ongoing discussion about algorithmic accountability. Within computer science, a new form of producing accountability has been discussed recently: causality as an expression of algorithmic accountability, formalized using structural causal models (SCMs). However, causality itself is a concept that needs further exploration. Therefore, in this contribution we confront ideas of SCMs with insights from social theory, more explicitly pragmatism, and argue that formal expressions of causality must always be seen in the context of the social system in which they are applied. This results in the formulation of further research questions and directions.

Causal Models are increasingly suggested as a mean 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.

Unlike faults in classical systems, faults in Cyber-Physical Systems will often be caused by the system's interaction with its physical environment and social context, rendering these faults harder to diagnose. To complicate matters further, knowledge about the behavior and failure modes of a system are often collected in different models. We show how three of those models, namely attack trees, fault trees, and timed failure propagation graphs can be converted into Halpern-Pearl causal models, combined into a single holistic causal model, and analyzed with actual causality reasoning to detect and explain unwanted events. Halpern-Pearl models have several advantages over their source models, particularly that they allow for modeling preemption, consider the non-occurrence of events, and can incorporate additional domain knowledge. Furthermore, such holistic models allow for analysis across model boundaries, enabling detection and explanation of events that are beyond a single model. Our contribution here delineates a semi-automatic process to (1) convert different models into Halpern-Pearl causal models, (2) combine these models into a single holistic model, and (3) reason about system failures. We illustrate our approach with the help of an Unmanned Aerial Vehicle case study.