2026-07-21T20:03:30Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:22755 2025-10-02T11:26:03Z ddc:004 open_access

AI Assessment in Practice: Implementing a Certification Scheme for AI Trustworthiness (Academic Track) Frischknecht-Gruber, Carmen Denzel, Philipp Reif, Monika Billeter, Yann Brunner, Stefan Forster, Oliver Schilling, Frank-Peter Weng, Joanna Chavarriaga, Ricardo AI Assessment Certification Scheme Artificial Intelligence Trustworthiness of AI systems AI Standards AI Safety The trustworthiness of artificial intelligence systems is crucial for their widespread adoption and for avoiding negative impacts on society and the environment. This paper focuses on implementing a comprehensive certification scheme developed through a collaborative academic-industry project. The scheme provides practical guidelines for assessing and certifying the trustworthiness of AI-based systems. The implementation of the scheme leverages aspects from Machine Learning Operations and the requirements management tool Jira to ensure continuous compliance and efficient lifecycle management. The integration of various high-level frameworks, scientific methods, and metrics supports the systematic evaluation of key aspects of trustworthiness, such as reliability, transparency, safety and security, and human oversight. These methods and metrics were tested and assessed on real-world use cases to dependably verify means of compliance with regulatory requirements and evaluate criteria and detailed objectives for each of these key aspects. Thus, this certification framework bridges the gap between ethical guidelines and practical application, ensuring the safe and effective deployment of AI technologies. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Carmen Frischknecht-Gruber and Philipp Denzel and Monika Reif and Yann Billeter and Stefan Brunner and Oliver Forster and Frank-Peter Schilling and Joanna Weng and Ricardo Chavarriaga 2025 Is Part Of OASIcs, Volume 126, Symposium on Scaling AI Assessments (SAIA 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.SAIA.2024.15 urn:nbn:de:0030-drops-227554 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SAIA.2024.15 eng https://creativecommons.org/licenses/by/4.0/legalcode