  The Department of Defense (DoD) has significantly increased its investment in
the design, evaluation, and deployment of Artificial Intelligence and Machine
Learning (AI/ML) capabilities to address national security needs. While there
are numerous AI/ML successes in the academic and commercial sectors, many of
these systems have also been shown to be brittle and nonrobust. In a complex
and ever-changing national security environment, it is vital that the DoD
establish a sound and methodical process to evaluate the performance and
robustness of AI/ML models before these new capabilities are deployed to the
field. This paper reviews the AI/ML development process, highlights common best
practices for AI/ML model evaluation, and makes recommendations to DoD
evaluators to ensure the deployment of robust AI/ML capabilities for national
security needs.
