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A Subjective Logic-based method for runtime confidence updates in safety arguments
Benjamin Her · 2026-05-23 · via cs.AI updates on arXiv.org

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Abstract:We present a method for dynamic quantitative assurance that enhances static safety cases with continuous, runtime-driven confidence updates. The method quantifies and propagates confidence across the development lifecycle by integrating design-time evidence and windowed runtime Safety Performance Indicators (SPIs) within a single Subjective Logic (SL)-based assurance case. At runtime, SPI evidence is continuously evaluated, and targeted claims are updated using a rule that increases confidence in the absence of violations and imposes prompt penalties when violations occur. This design prioritizes safety-relevant responsiveness over exact classical Bayesian posterior updates. We demonstrate the method using a simulation-based construction zone assist function, focusing on an ML-based construction cone detection component, and show how confidence evolves as SPI evidence is observed in operation.
Comments: Accepted for publication at the 41st ACM/SIGAPP Symposium on Applied Computing (SAC 2026)
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.22530 [cs.AI]
  (or arXiv:2605.22530v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2605.22530

arXiv-issued DOI via DataCite (pending registration)

Journal reference: Proceedings of the 41st ACM/SIGAPP Symposium on Applied Computing (SAC '26), 2026
Related DOI: https://doi.org/10.1145/3748522.3779865

DOI(s) linking to related resources

Submission history

From: Jessica Kelly [view email]
[v1] Thu, 21 May 2026 14:20:36 UTC (2,734 KB)