4 citations · 9 across the 5 of their papers we have counts for
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cs.SE2022★ 1 cited
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…
cs.AI2022
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…
cs.LG2022★ 4 cited
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…