Hardening of Artificial Neural Networks for Use in Safety-Critical Applications - A Mapping Study. - Rasmus Adler; Mohammed Naveed Akram; Pascal Bauer; Patrik Feth; Pascal Gerber; Andreas Jedlitschka; Lisa Jöckel; Michael Kläs; Daniel Schneider 001 (CoRR, 2019)

A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models. - Pascal Gerber; Lisa Jöckel; Michael Kläs (SafeAI@AAAI, 2022)

Engineering Dynamic Risk and Capability Models to Improve Cooperation Efficiency Between Human Workers and Autonomous Mobile Robots in Shared Spaces. - Jan Reich; Pascal Gerber; Nishanth Laxman; Daniel Schneider 001; Takehito Ogata; Satoshi Otsuka; Tasuku Ishigooka (IMBSA, 2022)

Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning. - Janek Groß; Michael Kläs; Lisa Jöckel; Pascal Gerber (DSN-W, 2023)

Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning. - Lisa Jöckel; Michael Kläs; Janek Groß; Pascal Gerber; Markus Scholz; Jonathan Eberle; Marc Teschner; Daniel Seifert; Richard Hawkins; John Molloy; Jens Ottnad (PROFES (1), 2023)

Conformal Prediction and Uncertainty Wrapper: What Statistical Guarantees Can You Get for Uncertainty Quantification in Machine Learning? - Lisa Jöckel; Michael Kläs; Janek Groß; Pascal Gerber (SAFECOMP Workshops, 2023)