One of the difficulties of artificial intelligence is to ensure that model decisions are fair and
free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and …

Interpretable machine learning aims at unveiling the reasons behind predictions returned by
uninterpretable classifiers. One of the most valuable types of explanation consists of …

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

Practitioners increasingly use machine learning (ML) models, yet models have become
more complex and harder to understand. To understand complex models, researchers have …

Machine learning is increasingly used to inform decision making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …

We present a brief history of the field of interpretable machine learning (IML), give an
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …

Abstract Machine learning plays a role in many deployed decision systems, often in ways
that are difficult or impossible to understand by human stakeholders. Explaining, in a human …

End-to-end neural Natural Language Processing (NLP) models are notoriously difficult to
understand. This has given rise to numerous efforts towards model explainability in recent …

Deep learning models have achieved high performance across different domains, such as
medical decision-making, autonomous vehicles, decision support systems, among many …

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