We present a sensitivity analysis-based method for explaining prediction models that can be
applied to any type of classification or regression model. Its advantage over existing general …

We present a general method for explaining individual predictions of classification models.
The method is based on fundamental concepts from coalitional game theory and predictions …

Current research into algorithmic explanation methods for predictive models can be divided
into two main approaches: gradient-based approaches limited to neural networks and more …

A complexity of business dynamics often forces decision-makers to make decisions based
on subjective mental models, reflecting their experience. However, research has shown that …

Learning from data streams (incremental learning) is increasingly attracting research focus
due to many real-world streaming problems and due to many open challenges, among …

We examine counterfactual explanations for explaining the decisions made by model-based
AI systems. The counterfactual approach we consider defines an explanation as a set of the …

In this paper, we present a novel method for explaining the decisions of an arbitrary
classifier, independent of the type of classifier. The method works at the instance level …

Lack of understanding of the decisions made by model-based AI systems is one of the main
barriers for their adoption. We examine counterfactual explanations, which are becoming an …

Many high performance machine learning methods produce black box models, which do not
disclose their internal logic yielding the prediction. However, in many application domains …

Feature construction can contribute to comprehensibility and performance of machine
learning models. Unfortunately, it usually requires exhaustive search in the attribute space …