At the request of the Deputy Assistant Secretary of the Army for Research and Technology (DASA(RT)), the National Academies of Sciences, Engineering, and Medicine, under the Board on Army Research and Development (BOARD), will appoint a workshop planning committee. The planning committee will develop a workshop to explore how to improve the robustness of machine learning and other artificial intelligence (ML/AI) technologies to better enable their successful implementation in Command and Control (C2) operations. Senior tactical leaders (e.g., Battalion Commanders and their staffs) often work in austere environments with limited connectivity and incomplete information, and must trust the ML/AI algorithms that aid in their decision-making. The workshop will investigate opportunities for the Army to improve security, reliability, and transparency to foster soldier trust in ML/AI systems.
Workshop participants will be asked to comment on vulnerabilities and limitations of ML/AI systems and identify opportunities for both materiel (R&D investments) and non-materiel (Doctrine, Organization, Training, and Policy) solutions. The workshop will also explore opportunities to use AI tools in C2, training considerations, limitations and vulnerabilities. The workshop planning committee will organize a workshop consisting of a 2-day unclassified meeting and a 2-day classified meeting centered on the following framing topics:
1. Examples of how industry and other branches of the military have successfully integrated ML/AI tools into a C2 architecture, particularly in a Multi-Domain Operations (MDO) environment.
2. How does the Army define success? How does it measure progress in these areas? What gaps exist in the Army achieving success?
3. What obstacles exist to achieving success and how might the Army overcome them?
A classified proceedings of the presentations and discussions at the workshop will be prepared in accordance with NASEM institutional guidelines. An unclassified summary of the classified workshop will be prepared consistent with security review.