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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
We illustrate the application of the framework on an example excerpt concerned with the quality of the test data.
Uncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice
When systems use data-based models that are based on machine learning (ML), errors in their results cannot be ruled out.
Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning
As the use of Artificial Intelligence (AI) components in cyber-physical systems is becoming more common, the need for reliable system architectures arises.
Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception
This poses a challenge with respect to the realization of safety-critical perception tasks by means of DDMs.
Integrating Testing and Operation-related Quantitative Evidences in Assurance Cases to Argue Safety of Data-Driven AI/ML Components
Using the proposed argumentation structure might not only increase the integrity of assurance cases but may also allow claims on quantitative targets that would not be justifiable otherwise.
A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models
In conclusion, softening hard decision tree boundaries appears to be a trade-off decision.
Towards a Common Testing Terminology for Software Engineering and Data Science Experts
Analytical quality assurance, especially testing, is an integral part of software-intensive system development.
