Guideline for Trustworthy Artificial Intelligence -- AI Assessment Catalog

no code implementations • 20 Jun 2023 • Maximilian Poretschkin, Anna Schmitz, Maram Akila, Linara Adilova, Daniel Becker, Armin B. Cremers, Dirk Hecker, Sebastian Houben, Michael Mock, Julia Rosenzweig, Joachim Sicking, Elena Schulz, Angelika Voss, Stefan Wrobel

Artificial Intelligence (AI) has made impressive progress in recent years and represents a key technology that has a crucial impact on the economy and society.

A Survey on Uncertainty Toolkits for Deep Learning

no code implementations • 2 May 2022 • Maximilian Pintz, Joachim Sicking, Maximilian Poretschkin, Maram Akila

The success of deep learning (DL) fostered the creation of unifying frameworks such as tensorflow or pytorch as much as it was driven by their creation in return.

Uncertainty Quantification

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety

no code implementations • 29 Apr 2021 • Sebastian Houben, Stephanie Abrecht, Maram Akila, Andreas Bär, Felix Brockherde, Patrick Feifel, Tim Fingscheidt, Sujan Sai Gannamaneni, Seyed Eghbal Ghobadi, Ahmed Hammam, Anselm Haselhoff, Felix Hauser, Christian Heinzemann, Marco Hoffmann, Nikhil Kapoor, Falk Kappel, Marvin Klingner, Jan Kronenberger, Fabian Küppers, Jonas Löhdefink, Michael Mlynarski, Michael Mock, Firas Mualla, Svetlana Pavlitskaya, Maximilian Poretschkin, Alexander Pohl, Varun Ravi-Kumar, Julia Rosenzweig, Matthias Rottmann, Stefan Rüping, Timo Sämann, Jan David Schneider, Elena Schulz, Gesina Schwalbe, Joachim Sicking, Toshika Srivastava, Serin Varghese, Michael Weber, Sebastian Wirkert, Tim Wirtz, Matthias Woehrle

Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods.

Autonomous Driving BIG-bench Machine Learning