@InProceedings{trustbench,
author="Rutinowski, J{\'e}r{\^o}me
and Kl{\"u}ttermann, Simon
and Endendyk, Jan
and Reining, Christopher
and M{\"u}ller, Emmanuel",
editor="Longo, Luca
and Lapuschkin, Sebastian
and Seifert, Christin",
title="Benchmarking Trust: A Metric for Trustworthy Machine Learning",
booktitle="Explainable Artificial Intelligence",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="287--307",
abstract="In the evolving landscape of machine learning research, the concept of trustworthiness receives critical consideration, both concerning data and models. However, the lack of a universally agreed upon definition of the very concept of trustworthiness presents a considerable challenge. The lack of such a definition impedes meaningful exchange and comparison of results when it comes to assessing trust. To make matters worse, coming up with a quantifiable metric is currently hardly possible. In consequence, the machine learning community cannot operationalize the term, beyond its current state as a hardly graspable concept.",
isbn="978-3-031-63787-2"
}