Agua: A Concept-Based Explainer for Learning-Enabled Systems
Sagar Patel, Dongsu Han, Nina Narodytska, Sangeetha Abdu Jyothi · 2025
While deep learning offers superior performance in systems and networking, adoption is often hindered by difficulties in understanding and debugging. Explainability aims to bridge this gap by providing insight into the model's decisions. However, existing methods primarily identify the most influential input features, forcing operators to perform extensive manual analysis of low-level signals (e.g., buffer t - 1).