2022/07/23 by John W. Sipple, Sipple, John, Abdou Youssef +1 · 1 citation
Computer Science · #Explainable Artificial Intelligence (XAI) #Anomaly Detection Techniques and Applications #Adversarial Robustness in Machine Learning
paper · pdf · doi:10.48550/arxiv.2207.11564
The need for explainable AI (XAI) is well established but relatively little has been published outside of the supervised learning paradigm. This paper focuses on a principled approach to applying explainability and interpretability to the task of unsupervised anomaly detection. We argue that explainability is principally an algorithmic task and interpretability is principally a cognitive task, and draw on insights from the cognitive sciences to propose a general-purpose method for practical diagnosis using explained anomalies. We define Attribution Error, and demonstrate, using real-world labeled datasets, that our method based on Integrated Gradients (IG) yields significantly lower attribution errors than alternative methods.