In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation
criteria have been developed within the research field of explainable artificial intelligence …

With the advent of machine learning (ML) and deep learning (DL) empowered applications
for critical applications like healthcare, the questions about liability, trust, and interpretability …

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 …

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 …

Accurate prediction of electric load is crucial in power grid planning and management. In this
paper, we solve the electric load forecasting problem under extreme events such as …

The lack of interpretability is an inevitable problem when using neural network models in
real applications. In this paper, an explainable neural network based on generalized …

The prototypical NLP experiment trains a standard architecture on labeled English data and
optimizes for accuracy, without accounting for other dimensions such as fairness …

We show that adding differential privacy to Explainable Boosting Machines (EBMs), a recent
method for training interpretable ML models, yields state-of-the-art accuracy while protecting …

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

Abstract We introduce Locally Interpretable Tree Boosting (LitBoost), a tree boosting model
tailored to applications where the data comes from several heterogeneous yet known …