Machine Learning Systems in Nuclear Command, Control, and Communications Architecture: Opportunities, Limitations, and Recommendations for Strategic Commanders
Abstract
AI systems today and in the near future offer the potential to ease the time, uncertainty, and information deluge burdens that characterize NC2. At the same time, this emerging enabling technology is both limited and carries risk in its current technological state. The consequences of misapplication or error could be existential. As commanders look for ways to integrate these systems into their teams, they must do so deliberately and purposefully. This paper will assess the opportunities and challenges to apply ML techniques in NC2 systems. The results of this analysis show that although adoption challenges may exist, the potential to reduce errors and increase decision-making time are significant. As such, the paper will outline how the technology can be best adopted. It will offer recommendations that spans from development to employment in an effort to manage the risk associated with integrating the new technology. Ultimately, this paper does not aim to deter leaders, but instead highlight possible integration challenges so that this incredibly capable technology does not become stigmatized due to misapplications.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 04, 2019
- Accession Number
- AD1079957
Entities
People
- Johnathan Falcone
Organizations
- Naval Postgraduate School