Interpretability and explainability are crucial for machine learning (ML) and statistical
applications in medicine, economics, law, and natural sciences and form an essential …

The continuous advancement of Artificial Intelligence (AI) has been revolutionizing the
strategy of decision-making in different life domains. Regardless of this achievement, AI …

The field of explainable artificial intelligence (XAI) is gaining increasing importance in recent
years. As a consequence, several surveys have been published to explore the current state …

The need for interpretable and accountable intelligent systems grows along with the
prevalence of artificial intelligence (AI) applications used in everyday life. Explainable AI …

Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …

Data visualizations and narratives are often integrated to convey data stories effectively.
Among various data storytelling formats, data videos have been garnering increasing …

Interpretable machine learning models trade-off accuracy for simplicity to make explanations
more readable and easier to comprehend. Drawing from cognitive psychology theories in …

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 …

Machine learning (ML) recourse techniques are increasingly used in high-stakes domains,
providing end users with actions to alter ML predictions, but they assume ML developers …

Machine learning (ML) interpretability techniques can reveal undesirable patterns in data
that models exploit to make predictions-potentially causing harms once deployed. However …