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DOI: 10.4230/OASIcs.SAIA.2024.3

EAM Diagrams - A Framework to Systematically Describe AI Systems for Effective AI Risk Assessment (Academic Track)

Authors: Ronald Schnitzer, Andreas Hapfelmeier, and Sonja Zillner

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

Abstract

Artificial Intelligence (AI) is a transformative technology that offers new opportunities across various applications. However, the capabilities of AI systems introduce new risks, which require the adaptation of established risk assessment procedures. A prerequisite for any effective risk assessment is a systematic description of the system under consideration, including its inner workings and application environment. Existing system description methodologies are only partially applicable to complex AI systems, as they either address only parts of the AI system, such as datasets or models, or do not consider AI-specific characteristics at all. In this paper, we present a novel framework called EAM Diagrams for the systematic description of AI systems, gathering all relevant information along the AI life cycle required to support a comprehensive risk assessment. The framework introduces diagrams on three levels, covering the AI system’s environment, functional inner workings, and the learning process of integrated Machine Learning (ML) models.

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Ronald Schnitzer, Andreas Hapfelmeier, and Sonja Zillner. EAM Diagrams - A Framework to Systematically Describe AI Systems for Effective AI Risk Assessment (Academic Track). In Symposium on Scaling AI Assessments (SAIA 2024). Open Access Series in Informatics (OASIcs), Volume 126, pp. 3:1-3:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{schnitzer_et_al:OASIcs.SAIA.2024.3,
  author =	{Schnitzer, Ronald and Hapfelmeier, Andreas and Zillner, Sonja},
  title =	{{EAM Diagrams - A Framework to Systematically Describe AI Systems for Effective AI Risk Assessment}},
  booktitle =	{Symposium on Scaling AI Assessments (SAIA 2024)},
  pages =	{3:1--3:16},
  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.2024.3},
  URN =		{urn:nbn:de:0030-drops-227432},
  doi =		{10.4230/OASIcs.SAIA.2024.3},
  annote =	{Keywords: AI system description, AI risk assessment, AI auditability}
}

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EAM Diagrams - A Framework to Systematically Describe AI Systems for Effective AI Risk Assessment (Academic Track)

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