  Organisations that design and deploy artificial intelligence (AI) systems
increasingly commit themselves to high-level, ethical principles. However,
there still exists a gap between principles and practices in AI ethics. One
major obstacle organisations face when attempting to operationalise AI Ethics
is the lack of a well-defined material scope. Put differently, the question to
which systems and processes AI ethics principles ought to apply remains
unanswered. Of course, there exists no universally accepted definition of AI,
and different systems pose different ethical challenges. Nevertheless,
pragmatic problem-solving demands that things should be sorted so that their
grouping will promote successful actions for some specific end. In this
article, we review and compare previous attempts to classify AI systems for the
purpose of implementing AI governance in practice. We find that attempts to
classify AI systems found in previous literature use one of three mental model.
The Switch, i.e., a binary approach according to which systems either are or
are not considered AI systems depending on their characteristics. The Ladder,
i.e., a risk-based approach that classifies systems according to the ethical
risks they pose. And the Matrix, i.e., a multi-dimensional classification of
systems that take various aspects into account, such as context, data input,
and decision-model. Each of these models for classifying AI systems comes with
its own set of strengths and weaknesses. By conceptualising different ways of
classifying AI systems into simple mental models, we hope to provide
organisations that design, deploy, or regulate AI systems with the conceptual
tools needed to operationalise AI governance in practice.
