Outlier Explanation

Outlier explanation focuses on identifying and interpreting data points that deviate significantly from the norm, a crucial task across diverse fields impacting decision-making and model reliability. Current research emphasizes developing explainable AI methods, employing techniques like autoencoders, decision trees, and sum-product networks to generate understandable explanations for these outliers, often focusing on feature importance or rule-based interpretations. This work is driven by the need for trustworthy AI systems, particularly in safety-critical applications, and aims to improve both the accuracy of outlier detection and the transparency of the underlying reasoning. The insights gained are valuable for enhancing model robustness, identifying biases, and facilitating human understanding of complex datasets.