Root Causing Prediction Anomalies Using Explainable AI

arXiv:2403.02439v1 Announce Type: new
Abstract: This paper presents a novel application of explainable AI (XAI) for root-causing performance degradation in machine learning models that learn continuously from user engagement data. In such systems a single feature corruption can cause cascading feature, label and concept drifts. We have successfully applied this technique to improve the reliability of models used in personalized advertising. Performance degradation in such systems manifest as prediction anomalies in the models. These models are typically trained continuously using …

abstract application arxiv concept corruption cs.ai cs.lg data engagement explainable ai feature learn machine machine learning machine learning models novel paper performance prediction systems type user engagement xai