MC-HAMMER: Mission Command - Human-centered Analysis of Machine learning Methods for Effectiveness and Resilience

The Department of Defenses Third Offset Strategy is focused on ensuring military forces technological advantage over our adversaries. To achieve this goal, the DoD has identified five primary components, including a key need to leverage and integrate advanced algorithms and autonomous agents, capable of understanding their human counterparts in large systems. The Armys Common Operating Environment (COE) in general, and its Command Post Computing Environment (CP CE) in particular, seek to develop a consistent approach to integration and interoperability of applications and data, including those employing machine learning (ML). To enable an effective and resilient integration of ML technology into CP CE, Aptima and its partner, Apex, propose to conduct the Mission Command - Human-centered Analysis of Machine learning Methods for Effectiveness and Resilience (MC-HAMMER) study. MC-HAMMER will produce a principled method for assessing the applicability of ML to MC tasks and processes. This approach will rely on a coupled understanding of ML methods and MC cognitive work, and will be instantiated as a model of applicability that predicts costs, benefits, and risks of particular ML-MC pairings. This methodology will directly support decisions regarding how, when, and where to use ML algorithms and automated agents within MC systems and processes.