  Artificial Intelligence (AI) algorithms are increasingly providing decision
making and operational support across multiple domains. AI includes a wide
library of algorithms for different problems. One important notion for the
adoption of AI algorithms into operational decision process is the concept of
assurance. The literature on assurance, unfortunately, conceals its outcomes
within a tangled landscape of conflicting approaches, driven by contradicting
motivations, assumptions, and intuitions. Accordingly, albeit a rising and
novel area, this manuscript provides a systematic review of research works that
are relevant to AI assurance, between years 1985 - 2021, and aims to provide a
structured alternative to the landscape. A new AI assurance definition is
adopted and presented and assurance methods are contrasted and tabulated.
Additionally, a ten-metric scoring system is developed and introduced to
evaluate and compare existing methods. Lastly, in this manuscript, we provide
foundational insights, discussions, future directions, a roadmap, and
applicable recommendations for the development and deployment of AI assurance.
