  Although AI-based systems are increasingly being leveraged to provide value
to organizations, individuals, and society, significant attendant risks have
been identified. These risks have led to proposed regulations, litigation, and
general societal concerns.
  As with any promising technology, organizations want to benefit from the
positive capabilities of AI technology while reducing the risks. The best way
to reduce risks is to implement comprehensive AI lifecycle governance where
policies and procedures are described and enforced during the design,
development, deployment, and monitoring of an AI system. While support for
comprehensive governance is beginning to emerge, organizations often need to
identify the risks of deploying an already-built model without knowledge of how
it was constructed or access to its original developers.
  Such an assessment will quantitatively assess the risks of an existing model
in a manner analogous to how a home inspector might assess the energy
efficiency of an already-built home or a physician might assess overall patient
health based on a battery of tests. This paper explores the concept of a
quantitative AI Risk Assessment, exploring the opportunities, challenges, and
potential impacts of such an approach, and discussing how it might improve AI
regulations.
