Multisource AI scorecard table analysis of AMIGO

Various techniques, applications, and tools for space situational awareness (SSA) have been developed for specific functions that can provide decision support tools. The generality of tools to enable a user-defined operating picture (UDOP) enables analysis across a wide variety of applications. This paper explores the Multisource AI Scorecard Table (MAST) for artificial intelligence/machine learning methods. Using the MAST categories, the Adaptive Markov Inference Game Optimization (AMIGO) SSA tool is presented as an example. The analysis reveals the importance of human interaction in the task, user, and technology operations. Recent advances in artificial intelligence (AI) have led to an explosion of multimedia applications (e.g., computer vision (CV) and natural language processing (NLP)) for different domains such as commercial, industrial, and intelligence. In particular, the use of AI applications is often problematic because the opaque nature of most systems leads to an inability for a human to understand how the results came about. A reliance on "black boxes" to generate predictions and inform decisions but requires explainability. This paper explores how MAST can support human-machine interactions to support the design and development of SSA tools. After describing the elements of MAST, the use case for AMIGO explains the general rating concept for the community to consider and modify for the interpretability of advanced data analytics that support various elements of data awareness.