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.
@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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