Lisa Jöckel

A Framework for Building Uncertainty Wrappers for AI/ML-Based Data-Driven Components

Michael Kläs, Lisa Jöckel, Michael Kläs, 2020, SAFECOMP Workshops.

Handling Uncertainty in Collaborative Embedded Systems Engineering

Thorsten Weyer, Boris Wirtz, Sebastian Törsleff, 2021 .

Safe Traffic Sign Recognition through Data Augmentation for Autonomous Vehicles Software

Silverio Martínez-Fernández, Michael Kläs, Lisa Jöckel, 2019, 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C).

Increasing Trust in Data-Driven Model Validation - A Framework for Probabilistic Augmentation of Images and Meta-data Generation Using Application Scope Characteristics

Michael Kläs, Lisa Jöckel, 2019, SAFECOMP.

Hardening of Artificial Neural Networks for Use in Safety-Critical Applications - A Mapping Study pdf

Patrik Feth, Daniel Schneider, Andreas Jedlitschka, 2019, ArXiv.

Towards a Common Testing Terminology for Software Engineering and Artificial Intelligence Experts pdf

Thomas Bauer, Janek Groß, Michael Kläs, 2021, ArXiv.

Using Complementary Risk Acceptance Criteria to Structure Assurance Cases for Safety-Critical AI Components

Janek Groß, Michael Kläs, Rasmus Adler, 2021, AISafety@IJCAI.

Could We Relieve AI/ML Models of the Responsibility of Providing Dependable Uncertainty Estimates? A Study on Outside-Model Uncertainty Estimates

Michael Kläs, Lisa Jöckel, Michael Kläs, 2021, SAFECOMP.