Abstract Machine learning based decision making systems in safety critical areas place high
demands on the accuracy and generalization ability of the underlying model. A common …

Machine learning based decision making systems applied in safety critical areas require
reliable high certainty predictions. For this purpose, the system can be extended by an reject …

Abstaining classifiers have the option to refrain from providing a prediction for instances that
are difficult to classify. The abstention mechanism is designed to trade off the classifier's …

For some classification scenarios, it is desirable to use only those classification instances a
trained model associates with a high certainty. To evaluate such high-certainty instances …

With the wide adoption of machine learning techniques, requirements have evolved beyond
sheer high performance, often requiring models to be trustworthy. A common approach to …

For some classification scenarios, it is desirable to use only those classification instances
that a trained model associates with a high certainty. To obtain such high-certainty …

Machine learning is more and more applied in critical application areas like health and
driver assistance. To minimize the risk of wrong decisions, in such applications it is …

The 2024 edition of the European Conference on Machine Learning and Principles and
Practice of Knowledge Discovery in Databases (ECML PKDD 2024) was held in Vilnius …

Automated decision-making (ADM) systems used in high-stakes areas such as lending or
hiring often perpetuate biases present in their underlying data. Consequently, these systems …

Counterfactual Explanations (CE) is a type of eXplainable Artificial Intelligence (XAI) that
addresses the common question of what options a user has to achieve a desired output. In …