Counterfactual Explanation (CE) is a post-hoc explanation method that provides a
perturbation for altering the prediction result of a classifier. An individual can interpret the …

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 …

Interpretable machine learning has become a strong competitor for black-box models.
However, the possible loss of the predictive performance for gaining understandability is …

In the era of Industry 4.0, the use of Artificial Intelligence (AI) is widespread in occupational
settings. Since dealing with human safety, explainability and trustworthiness of AI are even …

Simple, sufficient explanations furnished by short decision lists can be useful for guiding
stakeholder actions. Unfortunately, this transparency can come at the expense of the higher …

A hybrid model involves the cooperation of an interpretable model and a complex black box.
At inference, any input of the hybrid model is assigned to either its interpretable or complex …

A key question in causal inference analyses is how to find subgroups with elevated
treatment effects. This paper takes a machine learning approach and introduces a …

In this paper we propose a method for the optimal allocation of observations between an
intrinsically explainable glass box model and a black box model. An optimal allocation being …

Machine learning techniques are increasingly used for high-stakes decision making, such
as college admissions, loan attribution or recidivism prediction. It is thus crucial to ensure …

A key question in causal inference analyses is how to find subgroups with elevated
treatment effects. This paper takes a machine learning approach and introduces a …