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Documents authored by Höhndorf, Lukas


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Practitioner Track
SafeAI-Kit: A Software Toolbox to Evaluate AI Systems with a Focus on Uncertainty Quantification (Practitioner Track)

Authors: Dominik Eisl, Bastian Bernhardt, Lukas Höhndorf, and Rafal Kulaga

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


Abstract
In the course of the practitioner track, the IABG toolbox safeAI-kit is presented with a focus on uncertainty quantification in machine learning. The safeAI-kit consists of five sub-modules that provide analyses for performance, robustness, dataset, explainability, and uncertainty. The development of these sub-modules take ongoing standardization activities into account.

Cite as

Dominik Eisl, Bastian Bernhardt, Lukas Höhndorf, and Rafal Kulaga. SafeAI-Kit: A Software Toolbox to Evaluate AI Systems with a Focus on Uncertainty Quantification (Practitioner Track). In Symposium on Scaling AI Assessments (SAIA 2024). Open Access Series in Informatics (OASIcs), Volume 126, pp. 6:1-6:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)


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@InProceedings{eisl_et_al:OASIcs.SAIA.2024.6,
  author =	{Eisl, Dominik and Bernhardt, Bastian and H\"{o}hndorf, Lukas and Kulaga, Rafal},
  title =	{{SafeAI-Kit: A Software Toolbox to Evaluate AI Systems with a Focus on Uncertainty Quantification}},
  booktitle =	{Symposium on Scaling AI Assessments (SAIA 2024)},
  pages =	{6:1--6:3},
  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.2024.6},
  URN =		{urn:nbn:de:0030-drops-227466},
  doi =		{10.4230/OASIcs.SAIA.2024.6},
  annote =	{Keywords: safeAI-kit, Evaluation of AI Systems, Uncertainty Quantification}
}
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