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Documents authored by Chavarriaga, Ricardo

Document
Academic Track
DOI: 10.4230/OASIcs.SAIA.2024.15
AI Assessment in Practice: Implementing a Certification Scheme for AI Trustworthiness (Academic Track)

Authors: Carmen Frischknecht-Gruber, Philipp Denzel, Monika Reif, Yann Billeter, Stefan Brunner, Oliver Forster, Frank-Peter Schilling, Joanna Weng, and Ricardo Chavarriaga

Published in: OASIcs, Volume 126, Symposium on Scaling AI Assessments (SAIA 2024)

Abstract
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.
Cite as

Carmen Frischknecht-Gruber, Philipp Denzel, Monika Reif, Yann Billeter, Stefan Brunner, Oliver Forster, Frank-Peter Schilling, Joanna Weng, and Ricardo Chavarriaga. AI Assessment in Practice: Implementing a Certification Scheme for AI Trustworthiness (Academic Track). In Symposium on Scaling AI Assessments (SAIA 2024). Open Access Series in Informatics (OASIcs), Volume 126, pp. 15:1-15:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{frischknechtgruber_et_al:OASIcs.SAIA.2024.15,
  author =	{Frischknecht-Gruber, Carmen and Denzel, Philipp and Reif, Monika and Billeter, Yann and Brunner, Stefan and Forster, Oliver and Schilling, Frank-Peter and Weng, Joanna and Chavarriaga, Ricardo},
  title =	{{AI Assessment in Practice: Implementing a Certification Scheme for AI Trustworthiness}},
  booktitle =	{Symposium on Scaling AI Assessments (SAIA 2024)},
  pages =	{15:1--15:18},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-357-7},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{126},
  editor =	{G\"{o}rge, Rebekka and Haedecke, Elena and Poretschkin, Maximilian and Schmitz, Anna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SAIA.2024.15},
  URN =		{urn:nbn:de:0030-drops-227554},
  doi =		{10.4230/OASIcs.SAIA.2024.15},
  annote =	{Keywords: AI Assessment, Certification Scheme, Artificial Intelligence, Trustworthiness of AI systems, AI Standards, AI Safety}
}
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