Explainability (XAI) has matured in recent years to provide more human-centered
explanations of AI-based decision systems. While static explanations remain predominant …

Whether AI explanations can help users achieve specific tasks efficiently (ie, usable
explanations) is significantly influenced by their visual presentation. While many techniques …

Abstract Nowadays Artificial Intelligence (AI) has become a fundamental component of
healthcare applications, both clinical and remote, but the best performing AI systems are …

Explainable artificial intelligence (XAI) has emerged as a promising solution for addressing
the implementation challenges of AI/ML in healthcare. However, little is known about how …

The past decade has witnessed a plethora of works that leverage the power of visualization
(VIS) to interpret machine learning (ML) models. The corresponding research topic, VIS4ML …

Through extensive experience develo** and explaining machine learning (ML)
applications for real-world domains, we have learned that ML models are only as …

Large Language Models (LLMs) are notorious for blending fact with fiction and generating
non-factual content, known as hallucinations. To address this challenge, we propose an …

In history research, cohort analysis seeks to identify social structures and figure mobilities by
studying the group-based behavior of historical figures. Prior works mainly employ automatic …

Anomaly detection on time series data is increasingly common across various industrial
domains that monitor metrics in order to prevent potential accidents and economic losses …

Detecting anomalies in time-varying multivariate data is crucial in various industries for the
predictive maintenance of equipment. Numerous machine learning (ML) algorithms have …