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 …

Machine learning models in safety-critical settings like healthcare are often “blackboxes”:
they contain a large number of parameters which are not transparent to users. Post-hoc …

Classification and regression trees, as well as their variants, are off-the-shelf methods in
Machine Learning. In this paper, we review recent contributions within the Continuous …

Deep Learning (DL) has gained enormous popularity recently; however, it is an opaque
technique that is regarded as a black box. To ensure the validity of the model's prediction, it …

Shapley values provide model agnostic feature attributions for model outcome at a particular
instance by simulating feature absence under a global population distribution. The use of a …

Interpreting machine learning models is facilitated by the widely employed locally
interpretable model-agnostic explanation (LIME) technique. However, when extending LIME …

On the one hand, artificial neural networks (ANNs) are commonly labelled as black-boxes,
lacking interpretability; an issue that hinders human understanding of ANNs' behaviors. A …

Currently, deep learning models are commonly used for Aspect-Based Sentiment Analysis
(ABSA). These deep learning models are often seen as black boxes, meaning that they are …

Over the last years, the predictive power of supervised machine learning (ML) has
undergone impressive advances, achieving the status of state of the art and super-human …

A fundamental question on the use of ML models concerns the explanation of their
predictions for increasing transparency in decision-making. Although several interpretability …