4 citations · 7 across the 4 of their papers we have counts for
4 papers
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 +8
Assurance Cases (ACs) are an established approach in safety engineering to argue quality claims in a structured way. In the context of quality assurance for Machine Learning (ML)-b…
Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning
Janek Groß, Michael Kläs, Lisa Jöckel +1
As the use of Artificial Intelligence (AI) components in cyber-physical systems is becoming more common, the need for reliable system architectures arises. While data-driven models…
A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models
Pascal Gerber, Lisa Jöckel, Michael Kläs
Outcomes of data-driven AI models cannot be assumed to be always correct. To estimate the uncertainty in these outcomes, the uncertainty wrapper framework has been proposed, which…
Hardening of Artificial Neural Networks for Use in Safety-Critical Applications -- A Mapping Study
Rasmus Adler, Mohammed Naveed Akram, Pascal Bauer +6
Context: Across different domains, Artificial Neural Networks (ANNs) are used more and more in safety-critical applications in which erroneous outputs of such ANN can have catastro…