  Central to the concept of multi-domain operations (MDO) is the utilization of
an intelligence, surveillance, and reconnaissance (ISR) network consisting of
overlapping systems of remote and autonomous sensors, and human intelligence,
distributed among multiple partners. Realising this concept requires
advancement in both artificial intelligence (AI) for improved distributed data
analytics and intelligence augmentation (IA) for improved human-machine
cognition. The contribution of this paper is threefold: (1) we map the
coalition situational understanding (CSU) concept to MDO ISR requirements,
paying particular attention to the need for assured and explainable AI to allow
robust human-machine decision-making where assets are distributed among
multiple partners; (2) we present illustrative vignettes for AI and IA in MDO
ISR, including human-machine teaming, dense urban terrain analysis, and
enhanced asset interoperability; (3) we appraise the state-of-the-art in
explainable AI in relation to the vignettes with a focus on human-machine
collaboration to achieve more rapid and agile coalition decision-making. The
union of these three elements is intended to show the potential value of a CSU
approach in the context of MDO ISR, grounded in three distinct use cases,
highlighting how the need for explainability in the multi-partner coalition
setting is key.
