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

Author:

Jöckel Lisa,Kläs Michael,Groß Janek,Gerber Pascal,Scholz Markus,Eberle Jonathan,Teschner Marc,Seifert Daniel,Hawkins Richard,Molloy John,Ottnad Jens

Publisher

Springer Nature Switzerland

Reference24 articles.

1. GSN Community Standard Version 1 (2011). https://scsc.uk/r141:1?t=1. Accessed 28 July 2023

2. Feather, M.S., Slingerland, P.C., Guerrini, S., Spolaor, M.: Assurance guidance for machine learning in a safety-critical system. In: WAAM 2022 (2022)

3. Kläs, M., Adler, R., Jöckel, L., Groß, J., Reich, J.: Using complementary risk acceptance criteria to structure assurance cases for safety-critical AI components. In: AISafety 2021 (2021)

4. Hawkins, R., et al.: Guidance on the assurance of machine learning in autonomous systems (AMLAS). arXiv preprint arXiv:2102.01564 (2021)

5. ASCE Software Overview. https://www.adelard.com/asce/. Accessed 28 July 2023

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