Towards Trustworthy AI Engineering - A Case Study on integrating an AI audit catalog into MLOps processes-Reference-Cited by-同舟云学术

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Towards Trustworthy AI Engineering - A Case Study on integrating an AI audit catalog into MLOps processes

Published:2024-04-16 Issue: Volume: Page:1-7
ISSN:
Container-title:Proceedings of the 2nd International Workshop on Responsible AI Engineering
language:
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Author:

Helmer Lennard¹^ORCID,Martens Claudio¹^ORCID,Wegener Dennis¹^ORCID,Akila Maram¹^ORCID,Becker Daniel¹^ORCID,Abbas Sermad²^ORCID

Affiliation:

1. Fraunhofer Institute for Intelligent Analysis and Information Systems, Sankt Augustin, Germany

2. Indepenent, Sankt Augustin, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3643691.3648584

Reference21 articles.

1. Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements

2. Lameck Mbangula Amugongo, Alexander Kriebitz, Auxane Boch, and Christoph Lütge. 2023. Operationalising AI ethics through the agile software development lifecycle: a case study of AI-enabled mobile health applications. AI and Ethics (2023), 1--18.

3. Niklas Beck Claudio Martens Karl-Heinz Sylla Dennis Wegener and Alexander Zimmermann. 2020. Zukunftssichere Lösungen für maschinelles Lernen. (2020).

4. DevOps

5. Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014).

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