  Artificial intelligence systems, which are designed with a capability to
learn from the data presented to them, are used throughout society. These
systems are used to screen loan applicants, make sentencing recommendations for
criminal defendants, scan social media posts for disallowed content and more.
Because these systems don't assign meaning to their complex learned correlation
network, they can learn associations that don't equate to causality, resulting
in non-optimal and indefensible decisions being made. In addition to making
decisions that are sub-optimal, these systems may create legal liability for
their designers and operators by learning correlations that violate
anti-discrimination and other laws regarding what factors can be used in
different types of decision making. This paper presents the use of a machine
learning expert system, which is developed with meaning-assigned nodes (facts)
and correlations (rules). Multiple potential implementations are considered and
evaluated under different conditions, including different network error and
augmentation levels and different training levels. The performance of these
systems is compared to random and fully connected networks.
