Evaluating the influence of simulation fidelity in infrared sensor modeling on machine learning performance using explainable AI
Justin T. Carrillo, Barbara Pilate, Andrew C. Trautz, Matthew Bray, Jonathan D. Sherburn, Madeline S. Karr, Orie M. Cecil, Matthew W. Farthing · 2024
The increasing deployment of AI in critical sectors necessitates advancements in explainable AI (XAI) to ensure transparency and trustworthiness of AI decisions. This paper introduces a novel methodology that leverages the Virtual Environmental Simulation for Physics-based Analysis (VESPA) framework in conjunction with Randomized Input Sampling for Explanation (RISE) to provide enhanced explainability for AI models, particularly in complex simulated environments. VESPA, known for its high-fidelity, physics-based simulations across diverse conditions, generates a vast dataset encompassing various sensor configurations, environmental factors, and material responses. This dataset serves as the foundation for applying RISE, a model-agnostic approach that generates pixel-level importance maps by probing the AI model with masked versions of the input images. Through this integration, we offer a systematic way to visualize and understand the influence of different environmental elements on AI decisions. Our approach not only sheds light on the ”black box” of AI decision-making processes but also provides a scalable framework for evaluating AI models’ robustness and reliability under a wide array of simulated scenarios.