Abstract A popular explainable AI (XAI) approach to quantify feature importance of a given
model is via Shapley values. These Shapley values arose in cooperative games, and hence …

Different explainable AI (XAI) methods are based on different notions of 'ground truth'. In
order to trust explanations of AI systems, the ground truth has to provide fidelity towards the …

Global feature effect methods, such as partial dependence plots, provide an intelligible
visualization of the expected marginal feature effect. However, such global feature effect …

Explainable Artificial Intelligence (XAI) has received widespread interest in recent years, and
two of the most popular types of explanations are feature attributions, and counterfactual …

Shapley values originated in cooperative game theory but are extensively used today as a
model-agnostic explanation framework to explain predictions made by complex machine …

In this paper the goal is to explain predictions from complex machine learning models. One
method that has become very popular during the last few years is Shapley values. The …

Abstract Machine Learning explainability techniques have been proposed as a means of
'explaining'or interrogating a model in order to understand why a particular decision or …

While shallow decision trees may be interpretable, larger ensemble models like gradient-
boosted trees, which often set the state of the art in machine learning problems involving …

Abstract Although Shapley Values (SV) are widely used in explainable AI, they can be
poorly understood and estimated, implying that their analysis may lead to spurious …

While preference modelling is becoming one of the pillars of machine learning, the problem
of preference explanation remains challenging and underexplored. In this paper, we …