Expressing Accountability Patterns using Structural Causal Models
This work addresses the problem of ensuring accountability in systems like robots or drones that interact with human society, though it is incremental as it applies an existing modeling framework to a new domain.
The paper tackles the lack of formal methods to express accountability properties in cyber-physical systems, proposing the use of Structural Causal Models to represent and analyze these properties, which enables the identification of accountability patterns for system improvement.
While the exact definition and implementation of accountability depend on the specific context, at its core accountability describes a mechanism that will make decisions transparent and often provides means to sanction "bad" decisions. As such, accountability is specifically relevant for Cyber-Physical Systems, such as robots or drones, that embed themselves into a human society, take decisions and might cause lasting harm. Without a notion of accountability, such systems could behave with impunity and would not fit into society. Despite its relevance, there is currently no agreement on its meaning and, more importantly, no way to express accountability properties for these systems. As a solution we propose to express the accountability properties of systems using Structural Causal Models. They can be represented as human-readable graphical models while also offering mathematical tools to analyze and reason over them. Our central contribution is to show how Structural Causal Models can be used to express and analyze the accountability properties of systems and that this approach allows us to identify accountability patterns. These accountability patterns can be catalogued and used to improve systems and their architectures.