  We outline the principles of classical assurance for computer-based systems
that pose significant risks. We then consider application of these principles
to systems that employ Artificial Intelligence (AI) and Machine Learning (ML).
  A key element in this "dependability" perspective is a requirement to have
near-complete understanding of the behavior of critical components, and this is
considered infeasible for AI and ML. Hence the dependability perspective aims
to minimize trust in AI and ML elements by using "defense in depth" with a
hierarchy of less complex systems, some of which may be highly assured
conventionally engineered components, to "guard" them. This may be contrasted
with the "trustworthy" perspective that seeks to apply assurance to the AI and
ML elements themselves.
  In cyber-physical and many other systems, it is difficult to provide guards
that do not depend on AI and ML to perceive their environment (e.g., other
vehicles sharing the road with a self-driving car), so both perspectives are
needed and there is a continuum or spectrum between them. We focus on
architectures toward the dependability end of the continuum and invite others
to consider additional points along the spectrum.
  For guards that require perception using AI and ML, we examine ways to
minimize the trust placed in these elements; they include diversity, defense in
depth, explanations, and micro-ODDs. We also examine methods to enforce
acceptable behavior, given a model of the world. These include classical
cyber-physical calculations and envelopes, and normative rules based on
overarching principles, constitutions, ethics, or reputation. We apply our
perspective to autonomous systems, AI systems for specific functions, generic
AI such as Large Language Models, and to Artificial General Intelligence (AGI),
and we propose current best practice and an agenda for research.
