{"id":524124,"date":"2025-07-07T15:12:40","date_gmt":"2025-07-07T13:12:40","guid":{"rendered":"https:\/\/silicon-saxony.de\/bsi-methodological-guidelines-on-data-quality-in-ai-systems-presented\/"},"modified":"2025-07-07T15:12:40","modified_gmt":"2025-07-07T13:12:40","slug":"bsi-methodological-guidelines-on-data-quality-in-ai-systems-presented","status":"publish","type":"post","link":"https:\/\/silicon-saxony.de\/en\/bsi-methodological-guidelines-on-data-quality-in-ai-systems-presented\/","title":{"rendered":"BSI: Methodological guidelines on data quality in AI systems presented"},"content":{"rendered":"

<\/p>\n

BSI President Claudia Plattner: “We must ensure that applications with artificial intelligence meet high quality requirements. This is the only way we can produce and use trustworthy AI. With this catalog, the BSI is offering very specific assistance that starts at the grassroots level.”<\/p>\n

QUAIDAL is aimed in particular at providers of high-risk AI systems, for which the AI Regulation defines detailed requirements in terms of documentation, data management and continuous quality assurance. The modular design of the guideline enables project managers and development teams to select suitable measures for ensuring data quality at an early stage and systematically verify their implementation. In addition, this modular concept can be flexibly expanded in the future to take new technological developments into account. As the national cyber security authority, the BSI uses QUAIDAL to support public bodies and companies in implementing regulatory requirements and developing trustworthy AI applications.<\/p>\n

The draft of the guidelines is now available on the BSI website and in two machine-readable GitHub repositories. The community is invited to actively participate and contribute suggestions for their own building blocks, measures and metrics.<\/p>\n

– – – – – –<\/p>\n

Further links<\/h4>\n

\ud83d\udc49 www.bsi.bund.de<\/a>  

Photo: pixabay<\/i><\/p>\n","protected":false},"excerpt":{"rendered":"

July 1, 2025. The European AI regulation defines quality requirements for AI training data that cover aspects such as relevance, accuracy and completeness. The German Federal Office for Information Security (BSI) has now published a catalog for the quality assurance of training data in AI applications. The aim of the Qualitycriteria for AI Trainingdata in AI Lifecycle (QUAIDAL) document series is to systematically translate these abstract quality requirements into concrete building blocks, measures and metrics & methods. This structured approach supports targeted compliance with regulatory requirements and increases their technical traceability in the development process of AI systems.<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[4764],"tags":[4759,244,250],"class_list":["post-524124","post","type-post","status-publish","format-standard","hentry","category-software-en","tag-artificial-intelligence-ai","tag-big-data","tag-it"],"acf":[],"yoast_head":"\nBSI: Methodological guidelines on data quality in AI systems presented - Silicon Saxony<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/silicon-saxony.de\/en\/bsi-methodological-guidelines-on-data-quality-in-ai-systems-presented\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"BSI: Methodological guidelines on data quality in AI systems presented - Silicon Saxony\" \/>\n<meta property=\"og:description\" content=\"July 1, 2025. The European AI regulation defines quality requirements for AI training data that cover aspects such as relevance, accuracy and completeness. The German Federal Office for Information Security (BSI) has now published a catalog for the quality assurance of training data in AI applications. The aim of the Qualitycriteria for AI Trainingdata in AI Lifecycle (QUAIDAL) document series is to systematically translate these abstract quality requirements into concrete building blocks, measures and metrics & methods. 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