  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.
