The fields of Artificial Intelligence (AI) and Machine Learning (ML) are poised to revolutionize the scale, efficiency, and complexity of Department of Defense (DoD) operations. However, when considering the impact of a failure in an automated DoD application, it becomes clear that there is a need for AI and ML systems that can be trusted. One way to build trust is through AI/ML models that have increased transparency in their underlying processes and performance. In this work we address research on explainable artificial intelligence (XAI) and machine learning that fails predictably (i.e., more accurate confidence intervals) to understand the effects of transparency of process and performance, respectively. The research conducted under this project will focus on the user psychological perceptions of new XAI and predictable failure algorithms.