Preprint about the trustworthiness landscape in machine learning

We are happy to announce that the preprint of our paper “The Trustworthiness Landscape in Machine Learning: A Conceptual Guide with Applications in Medicine” is finally live on Zenodo: https://zenodo.org/records/17591544. It explains more than 17 concepts related to the trustworthiness of machine learning (ML), including, e.g., reliability, robustness, fairness, interpretability/explainability, security and privacy, and explores how these concepts connect and conflict. It is targeted at anyone who wants to use ML responsibly, and especially researchers who encounter ML-based science in their daily work (not necessarily as developers). No extensive ML background is expected.

The project was lead by first author Prof. Dr. Kerstin Lenhof, the head of the AI and multi-omics data group at the University Medicine Göttingen (UMG). The paper was writtin in collaboration with many amazing researchers from different backgrounds and insitutions: Lorina Buhr (UMG), Sebastian Roth (University of Bayreuth), Ruta Binkyte(CISPA), Silke Schicktanz (UMG), Mario Fritz (CISPA), and Niko Beerenwinkel (ETH Zürich).