Researchers have created a taxonomy and outlined steps that developers can take to design features in machine-learning models that are easier for decision-makers to understand. Explanation methods ...

The field of interpretability investigates what machine learning (ML) models are learning from training datasets, the causes and effects of changes within a model, and the justifications behind its ...

Deep machine learning has achieved remarkable success in various fields of artificial intelligence, but achieving both high interpretability and high efficiency simultaneously remains a critical ...

Two of the biggest questions associated with AI are “why does AI do what it does”? and “how does it do it?” Depending on the context in which the AI algorithm is used, those questions can be mere ...