Abstract The XAI Disagreement Problem concerns the fact that various explainability
methods yield different local/global insights on model behavior. Thus, given the lack of …

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

Abstract Accumulated Local Effects (ALE) is a widely-used explainability method for isolating
the average effect of a feature on the output, because it handles cases with correlated …

We study the robustness of global post-hoc explanations for predictive models trained on
tabular data. Effects of predictor features in black-box supervised learning are an essential …

Machine learning research focuses on the improvement of prediction performance. Progress
was made with black-box models that flexibly adapt to the given data. However, due to their …

Post-hoc explanation techniques such as the well-established partial dependence plot
(PDP), which investigates feature dependencies, are used in explainable artificial …

Generalized Additive Models (GAMs) are widely used explainable-by-design models in
various applications. GAMs assume that the output can be represented as a sum of …

Surrogate models play a crucial role in retrospectively interpreting complex and powerful
black box machine learning models via model distillation. This paper focuses on using …

The adoption of artificial intelligence (AI) across industries has led to the widespread use of
complex black-box models and interpretation tools for decision making. This paper proposes …

Regional effect is a novel explainability method that can be used for automated tabular data
understanding through a three-step procedure; a black-box machine learning (ML) model is …