The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

Feature attributions based on the Shapley value are popular for explaining machine
learning models. However, their estimation is complex from both theoretical and …

Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …

Over the last few years, the Shapley value, a solution concept from cooperative game theory,
has found numerous applications in machine learning. In this paper, we first discuss …

With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …

Explainable machine learning offers the potential to provide stakeholders with insights into
model behavior by using various methods such as feature importance scores, counterfactual …

Deep learning as performed by artificial deep neural networks (DNNs) has achieved great
successes recently in many important areas that deal with text, images, videos, graphs, and …

Acute critical illness is often preceded by deterioration of routinely measured clinical
parameters, eg, blood pressure and heart rate. Early clinical prediction is typically based on …

In addition to the impressive predictive power of machine learning (ML) models, more
recently, explanation methods have emerged that enable an interpretation of complex …

The Shapley value concept from cooperative game theory has become a popular technique
for interpreting ML models, but efficiently estimating these values remains challenging …