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 …

Existing foundation models are trained on copyrighted material. Deploying these models
can pose both legal and ethical risks when data creators fail to receive appropriate …

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 …

Nowadays, deep neural networks are widely used in mission critical systems such as
healthcare, self-driving vehicles, and military which have direct impact on human lives …

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 …

Tree-based machine learning models such as random forests, decision trees and gradient
boosted trees are popular nonlinear predictive models, yet comparatively little attention has …

A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature
relevance techniques to explain a deep neural network (DNN) output or explaining models …

Artificial intelligence (AI) is widely heralded as a new and revolutionary technology that will
transform the world of work. While the impact of AI on human resource (HR) and people …

This book is about making machine learning models and their decisions interpretable. After
exploring the concepts of interpretability, you will learn about simple, interpretable models …