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 …

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 …

While machine learning (ML) models are increasingly used due to their high predictive
power, their use in understanding the data-generating process (DGP) is limited …

Global feature effect methods explain a model outputting one plot per feature. The plot
shows the average effect of the feature on the output, like the effect of age on the annual …

Explainable artificial intelligence or interpretable machine learning techniques aim to shed
light on the behavior of opaque machine learning algorithms, yet often fail to acknowledge …

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 …