Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915489120256000 |
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| author | Schweighofer, Kajetan Brune, Barbara Gruber, Lukas Schmid, Simon Aufreiter, Alexander Gruber, Andreas Doms, Thomas Eder, Sebastian Mayer, Florian Stadlbauer, Xaver-Paul Schwald, Christoph Zellinger, Werner Nessler, Bernhard Hochreiter, Sepp |
| author_facet | Schweighofer, Kajetan Brune, Barbara Gruber, Lukas Schmid, Simon Aufreiter, Alexander Gruber, Andreas Doms, Thomas Eder, Sebastian Mayer, Florian Stadlbauer, Xaver-Paul Schwald, Christoph Zellinger, Werner Nessler, Bernhard Hochreiter, Sepp |
| contents | There is an increasing adoption of artificial intelligence in safety-critical applications, yet practical schemes for certifying that AI systems are safe, lawful and socially acceptable remain scarce. This white paper presents the TÜV AUSTRIA Trusted AI framework an end-to-end audit catalog and methodology for assessing and certifying machine learning systems. The audit catalog has been in continuous development since 2019 in an ongoing collaboration with scientific partners. Building on three pillars - Secure Software Development, Functional Requirements, and Ethics & Data Privacy - the catalog translates the high-level obligations of the EU AI Act into specific, testable criteria. Its core concept of functional trustworthiness couples a statistically defined application domain with risk-based minimum performance requirements and statistical testing on independently sampled data, providing transparent and reproducible evidence of model quality in real-world settings. We provide an overview of the functional requirements that we assess, which are oriented on the lifecycle of an AI system. In addition, we share some lessons learned from the practical application of the audit catalog, highlighting common pitfalls we encountered, such as data leakage scenarios, inadequate domain definitions, neglect of biases, or a lack of distribution drift controls. We further discuss key aspects of certifying AI systems, such as robustness, algorithmic fairness, or post-certification requirements, outlining both our current conclusions and a roadmap for future research. In general, by aligning technical best practices with emerging European standards, the approach offers regulators, providers, and users a practical roadmap for legally compliant, functionally trustworthy, and certifiable AI systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08852 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Schweighofer, Kajetan Brune, Barbara Gruber, Lukas Schmid, Simon Aufreiter, Alexander Gruber, Andreas Doms, Thomas Eder, Sebastian Mayer, Florian Stadlbauer, Xaver-Paul Schwald, Christoph Zellinger, Werner Nessler, Bernhard Hochreiter, Sepp Computers and Society Artificial Intelligence Machine Learning There is an increasing adoption of artificial intelligence in safety-critical applications, yet practical schemes for certifying that AI systems are safe, lawful and socially acceptable remain scarce. This white paper presents the TÜV AUSTRIA Trusted AI framework an end-to-end audit catalog and methodology for assessing and certifying machine learning systems. The audit catalog has been in continuous development since 2019 in an ongoing collaboration with scientific partners. Building on three pillars - Secure Software Development, Functional Requirements, and Ethics & Data Privacy - the catalog translates the high-level obligations of the EU AI Act into specific, testable criteria. Its core concept of functional trustworthiness couples a statistically defined application domain with risk-based minimum performance requirements and statistical testing on independently sampled data, providing transparent and reproducible evidence of model quality in real-world settings. We provide an overview of the functional requirements that we assess, which are oriented on the lifecycle of an AI system. In addition, we share some lessons learned from the practical application of the audit catalog, highlighting common pitfalls we encountered, such as data leakage scenarios, inadequate domain definitions, neglect of biases, or a lack of distribution drift controls. We further discuss key aspects of certifying AI systems, such as robustness, algorithmic fairness, or post-certification requirements, outlining both our current conclusions and a roadmap for future research. In general, by aligning technical best practices with emerging European standards, the approach offers regulators, providers, and users a practical roadmap for legally compliant, functionally trustworthy, and certifiable AI systems. |
| title | Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned |
| topic | Computers and Society Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.08852 |