30 citations · 32 across the 5 of their papers we have counts for
5 papers
Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning
Lisa Jöckel, Michael Kläs, Janek Groß +8
Assurance Cases (ACs) are an established approach in safety engineering to argue quality claims in a structured way. In the context of quality assurance for Machine Learning (ML)-b…
Uncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice
Lisa Jöckel, Michael Kläs, Georg Popp +2
When systems use data-based models that are based on machine learning (ML), errors in their results cannot be ruled out. This is particularly critical if it remains unclear to the…
Badgers: generating data quality deficits with Python
Julien Siebert, Daniel Seifert, Patricia Kelbert +2
Generating context specific data quality deficits is necessary to experimentally assess data quality of data-driven (artificial intelligence (AI) or machine learning (ML)) applicat…
Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning
Janek Groß, Michael Kläs, Lisa Jöckel +1
As the use of Artificial Intelligence (AI) components in cyber-physical systems is becoming more common, the need for reliable system architectures arises. While data-driven models…
Transparent Combination of Expert and Measurement Data for Defect Prediction: An Industrial Case Study
Michael Kläs, Frank Elberzhager, Jürgen Münch +2
Defining strategies on how to perform quality assurance (QA) and how to control such activities is a challenging task for organizations developing or maintaining software and softw…