Thanks to the exponential growth in computing power and vast amounts of data, artificial
intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be …

Artificial intelligence (AI) and machine learning (ML) have recently been radically improved
and are now being employed in almost every application domain to develop automated or …

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 …

Time series data is increasingly used in a wide range of fields, and it is often relied on in
crucial applications and high-stakes decision-making. For instance, sensors generate time …

A method for the local and global interpretation of a black-box model on the basis of the well-
known generalized additive models is proposed. It can be viewed as an extension or a …

The main objective of eXplainable Artificial Intelligence (XAI) is to provide effective
explanations for black-box classifiers. The existing literature lists many desirable properties …

We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series
classification. XEM relies on a new hybrid ensemble method that combines an explicit …

Heart disease is one of the leading causes of mortality throughout the world. Among the
different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non …

Counterfactual Analysis has shown to be a powerful tool in the burgeoning field of
Explainable Artificial Intelligence. In Supervised Classification, this means associating with …

Despite the high accuracy offered by state-of-the-art deep natural-language models (eg,
LSTM, BERT), their application in real-life settings is still widely limited, as they behave like a …