4 citations · 9 across the 5 of their papers we have counts for
5 papers
Uncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice
Lisa Jöckel, Michael Kläs, Georg Popp +2
When systems use data-based models that are based on machine learning (ML), errors in their results cannot be ruled out. This is particularly critical if it remains unclear to the…
Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception
Janek Groß, Rasmus Adler, Michael Kläs +3
Data-driven models (DDM) based on machine learning and other AI techniques play an important role in the perception of increasingly autonomous systems. Due to the merely implicit d…
Integrating Testing and Operation-related Quantitative Evidences in Assurance Cases to Argue Safety of Data-Driven AI/ML Components
Michael Kläs, Lisa Jöckel, Rasmus Adler +1
In the future, AI will increasingly find its way into systems that can potentially cause physical harm to humans. For such safety-critical systems, it must be demonstrated that the…
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…