  Deploying successful software-reliant systems that address their mission
goals and user needs within cost, resource, and expected quality constraints
require design trade-offs. These trade-offs dictate how systems are structured
and how they behave and consequently can effectively be evolved and sustained.
Software engineering practices address this challenge by centering system
design and evolution around delivering key quality attributes, such as
security, privacy, data centricity, sustainability, and explainability. These
concerns are more urgent requirements for software-reliant systems that also
include AI components due to the uncertainty introduced by data elements.
Moreover, systems employed by the public sector exhibit unique design time and
runtime challenges due to the regulatory nature of the domains. We assert that
the quality attributes of security, privacy, data centricity, sustainability,
and explainability pose new challenges to AI engineering and will drive the
success of AI-enabled systems in the public sector. In this position paper, we
enumerate with examples from healthcare domain concerns related to these
requirements to mitigate barriers to architecting and fielding AI-enabled
systems in the public sector.
