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Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning.

, , , , , , , , , , and . PROFES (1), volume 14483 of Lecture Notes in Computer Science, page 151-158. Springer, (2023)

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Hardening of Artificial Neural Networks for Use in Safety-Critical Applications - A Mapping Study., , , , , , , , and . CoRR, (2019)Conformal Prediction and Uncertainty Wrapper: What Statistical Guarantees Can You Get for Uncertainty Quantification in Machine Learning?, , , and . SAFECOMP Workshops, volume 14182 of Lecture Notes in Computer Science, page 314-327. Springer, (2023)Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning., , , and . DSN-W, page 231-238. IEEE, (2023)Engineering Dynamic Risk and Capability Models to Improve Cooperation Efficiency Between Human Workers and Autonomous Mobile Robots in Shared Spaces., , , , , , and . IMBSA, volume 13525 of Lecture Notes in Computer Science, page 237-251. Springer, (2022)A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models., , and . SafeAI@AAAI, volume 3087 of CEUR Workshop Proceedings, CEUR-WS.org, (2022)Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning., , , , , , , , , and 1 other author(s). PROFES (1), volume 14483 of Lecture Notes in Computer Science, page 151-158. Springer, (2023)