  This paper investigates the prospect of developing human-interpretable,
explainable artificial intelligence (AI) systems based on active inference and
the free energy principle. We first provide a brief overview of active
inference, and in particular, of how it applies to the modeling of
decision-making, introspection, as well as the generation of overt and covert
actions. We then discuss how active inference can be leveraged to design
explainable AI systems, namely, by allowing us to model core features of
``introspective'' processes and by generating useful, human-interpretable
models of the processes involved in decision-making. We propose an architecture
for explainable AI systems using active inference. This architecture
foregrounds the role of an explicit hierarchical generative model, the
operation of which enables the AI system to track and explain the factors that
contribute to its own decisions, and whose structure is designed to be
interpretable and auditable by human users. We outline how this architecture
can integrate diverse sources of information to make informed decisions in an
auditable manner, mimicking or reproducing aspects of human-like consciousness
and introspection. Finally, we discuss the implications of our findings for
future research in AI, and the potential ethical considerations of developing
AI systems with (the appearance of) introspective capabilities.
