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Assurance Cases (ACs) are an established approach in safety engineering toargue quality claims in a structured way. In the context of quality assurancefor Machine Learning (ML)-based software components, ACs are also beingdiscussed and appear promising. Tools for operationalizing ACs do exist, yetmainly focus on supporting safety engineers on the system level. However,assuring the quality of an ML component within the system is commonly theresponsibility of data scientists, who are usually less familiar with thesetools. To address this gap, we propose a framework to support theoperationalization of ACs for ML components based on technologies that datascientists use on a daily basis: Python and Jupyter Notebook. Our aim is tomake the process of creating ML-related evidence in ACs more effective. Resultsfrom the application of the framework, documented through notebooks, can beintegrated into existing AC tools. We illustrate the application of theframework on an example excerpt concerned with the quality of the test data.

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