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cs.AI2026
CUBICS: Situation-aware performance estimation for safety-relevant ML components
Benjamin Herd, Jessica Kelly, Mario Trapp
Machine learning (ML) is a key technology driving innovation today, but ensuring ML safety remains a major challenge for safety-related applications. A promising idea is to build p…
cs.AI2026
A Subjective Logic-based method for runtime confidence updates in safety arguments
Benjamin Herd, Jessica Kelly, Clarissa Heinemann +1
We present a method for dynamic quantitative assurance that enhances static safety cases with continuous, runtime-driven confidence updates. The method quantifies and propagates co…
cs.AI2026
Towards a compositional semantics for quantitative confidence assessment in assurance arguments
Benjamin Herd, Jessica Kelly, Jan Sabsch +1
Assurance arguments provide a clear and structured way to explain why stakeholders should trust that a system satisfies certain properties, yet widely used notations, e.g.Goal Stru…