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Documents authored by Merget, Iris

Document
Academic Track
DOI: 10.4230/OASIcs.SAIA.2024.7
Towards Trusted AI: A Blueprint for Ethics Assessment in Practice (Academic Track)

Authors: Christoph Tobias Wirth, Mihai Maftei, Rosa Esther Martín-Peña, and Iris Merget

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

Abstract
The development of AI technologies leaves place for unforeseen ethical challenges. Issues such as bias, lack of transparency and data privacy must be addressed during the design, development, and the deployment stages throughout the lifecycle of AI systems to mitigate their impact on users. Consequently, ensuring that such systems are responsibly built has become a priority for researchers and developers from both public and private sector. As a proposed solution, this paper presents a blueprint for AI ethics assessment. The blueprint provides for AI use cases an adaptable approach which is agnostic to ethics guidelines, regulatory environments, business models, and industry sectors. The blueprint offers an outcomes library of key performance indicators (KPIs) which are guided by a mapping of ethics framework measures to processes and phases defined by the blueprint. The main objectives of the blueprint are to provide an operationalizable process for the responsible development of ethical AI systems, and to enhance public trust needed for broad adoption of trusted AI solutions. In an initial pilot the blueprinted for AI ethics assessment is applied to a use case of generative AI in education.
Cite as

Christoph Tobias Wirth, Mihai Maftei, Rosa Esther Martín-Peña, and Iris Merget. Towards Trusted AI: A Blueprint for Ethics Assessment in Practice (Academic Track). In Symposium on Scaling AI Assessments (SAIA 2024). Open Access Series in Informatics (OASIcs), Volume 126, pp. 7:1-7:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{wirth_et_al:OASIcs.SAIA.2024.7,
  author =	{Wirth, Christoph Tobias and Maftei, Mihai and Mart{\'\i}n-Pe\~{n}a, Rosa Esther and Merget, Iris},
  title =	{{Towards Trusted AI: A Blueprint for Ethics Assessment in Practice}},
  booktitle =	{Symposium on Scaling AI Assessments (SAIA 2024)},
  pages =	{7:1--7:19},
  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.2024.7},
  URN =		{urn:nbn:de:0030-drops-227478},
  doi =		{10.4230/OASIcs.SAIA.2024.7},
  annote =	{Keywords: Trusted AI, Trustworthy AI, AI Ethics Assessment Framework, AI Quality, AI Ethics, AI Ethics Assessment, AI Lifecycle, Responsible AI, Ethics-By-Design, AI Risk Management, Ethics Impact Assessment, AI Ethics KPIs, Human-Centric AI, Applied Ethics}
}
Document
Academic Track
DOI: 10.4230/OASIcs.SAIA.2024.10
Evaluating Dimensions of AI Transparency: A Comparative Study of Standards, Guidelines, and the EU AI Act (Academic Track)

Authors: Sergio Genovesi, Martin Haimerl, Iris Merget, Samantha Morgaine Prange, Otto Obert, Susanna Wolf, and Jens Ziehn

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

Abstract
Transparency is considered a key property with respect to the implementation of trustworthy artificial intelligence (AI). It is also addressed in various documents concerned with the standardization and regulation of AI systems. However, this body of literature lacks a standardized, widely-accepted definition of transparency, which would be crucial for the implementation of upcoming legislation for AI like the AI Act of the European Union (EU). The main objective of this paper is to systematically analyze similarities and differences in the definitions and requirements for AI transparency. For this purpose, we define main criteria reflecting important dimensions of transparency. According to these criteria, we analyzed a set of relevant documents in AI standardization and regulation, and compared the outcomes. Almost all documents included requirements for transparency, including explainability as an associated concept. However, the details of the requirements differed considerably, e.g., regarding pieces of information to be provided, target audiences, or use cases with respect to the development of AI systems. Additionally, the definitions and requirements often remain vague. In summary, we demonstrate that there is a substantial need for clarification and standardization regarding a consistent implementation of AI transparency. The method presented in our paper can serve as a basis for future steps in the standardization of transparency requirements, in particular with respect to upcoming regulations like the European AI Act.
Cite as

Sergio Genovesi, Martin Haimerl, Iris Merget, Samantha Morgaine Prange, Otto Obert, Susanna Wolf, and Jens Ziehn. Evaluating Dimensions of AI Transparency: A Comparative Study of Standards, Guidelines, and the EU AI Act (Academic Track). In Symposium on Scaling AI Assessments (SAIA 2024). Open Access Series in Informatics (OASIcs), Volume 126, pp. 10:1-10:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{genovesi_et_al:OASIcs.SAIA.2024.10,
  author =	{Genovesi, Sergio and Haimerl, Martin and Merget, Iris and Prange, Samantha Morgaine and Obert, Otto and Wolf, Susanna and Ziehn, Jens},
  title =	{{Evaluating Dimensions of AI Transparency: A Comparative Study of Standards, Guidelines, and the EU AI Act}},
  booktitle =	{Symposium on Scaling AI Assessments (SAIA 2024)},
  pages =	{10:1--10:17},
  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.2024.10},
  URN =		{urn:nbn:de:0030-drops-227509},
  doi =		{10.4230/OASIcs.SAIA.2024.10},
  annote =	{Keywords: AI, transparency, regulation}
}
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