Interpreting Chemical Detection Alarms with Live Analysis of ML Algorithms

Chemical Biological Radiological Nuclear and Explosive (CBRNE) sensing systems in the field provide alarms in the form of simple graphical representations, lights, vibrations, and alarm sounds to maximize the reaction time of the user in the event of a hazardous situation. Artificial Intelligence (AI) can be used to reduce the false alarms of chemical detectors, allowing users to react with confidence when an alarm does occurs. However, the Department of Defense's AI ethics standards states that technologies incorporating AI systems be traceable, reliable, and governable. Given the complex nature of AI and the difficulties of interpreting results, testing and evaluating AI systems poses a challenge for CBRNE sensing systems. To properly interpret and evaluate AI systems it is imperative graphical user interfaces (GUI) are designed to be simple interfaces that provide easy to interpret results. Presented here is an interpretable alarm GUI for orthogonal networked sensors (IAGOnet). IAGOnet provides real-time status of connected sensors utilizing a familiar replication of their onboard results, along with simple to understand graphical representations of confidence metrics from machine learning (ML) predictions. IAGOnet allows a user to compare the detector's original alarm state to current and previous predictions of classification algorithms, thereby reducing the false alarms. Our work demonstrates the practical nature of IAGOnet by utilizing data from an ion mobility spectrometry (IMS) based detector and a multi-gas detector.