Provenance-Enabled Explainable AI

Machine learning (ML) algorithms have advanced significantly in recent years, progressively evolving into artificial intelligence (AI) agents capable of solving complex, human-like intellectual challenges. Despite the advancements, the interpretability of these sophisticated models lags behind, with many ML architectures remaining "black boxes" that are too intricate and expansive for human interpretation. Recognizing this issue, there has been a revived interest in the field of explainable AI (XAI) aimed at explaining these opaque ML models. However, XAI tools often suffer from being tightly coupled with the underlying ML models and are inefficient due to redundant computations. We introduce provenance-enabled explainable AI (PXAI). PXAI decouples XAI computation from ML models through a provenance graph that tracks the creation and transformation of all data within the model. PXAI improves XAI computational efficiency by excluding irrelevant and insignificant variables and computation in the provenance graph. Through various case studies, we demonstrate how PXAI enhances computational efficiency when interpreting complex ML models, confirming its potential as a valuable tool in the field of XAI.