Explainable Artificial Intelligence (XAI) for Air Traffic Management

Explainable AI (XAI) ndash; AI in which humans can understand the decisions or predictions made by the AI system ndash; is becoming critical for many applications, and in particular, for the aviation domain. However, Machine Learning (ML) systems too often resemble ldquo;black boxesrdquo; that mysteriously convert incoming data into predicted outcomes and recommended decisions. nbsp;This SBIR effort identifies a way to add explainability to AI and ML systems exploiting an XAI vocabulary that is unique to the Air Traffic Management (ATM) domain.nbsp; The solutionnbsp;leveragesnbsp;rules, regulations, and policies that shape ATM activities, that are inherently learned within AI and ML systems.nbsp; Given large quantities of historical 4-Dimensional (4D) Trajectory-Based Operations (TBO) data for training AI and ML systems, Intent Inference Learning (IIL) is used to establish XAI word quot;labelsquot; on each historical data point.nbsp; These labeled historical data points are used for the training of AI and ML systems, as well as for the explanations of the results of the AI or ML system.nbsp;nbsp;