AI Readiness of Standards: Bridging Traditional Norms with Modern Technologies (Practitioner Track)

In an era where artificial intelligence (AI) is spreading throughout most industries, it is imperative to understand how existing regulatory frameworks, particularly technical standards, can adapt to accommodate AI technologies. This paper presents findings of an interdisciplinary research & development project aimed at evaluating the AI readiness of the German national body of standards, encompassing approximately 30,000 DIN, DIN EN, and DIN EN ISO documents. Utilizing a hybrid approach that combines human expertise with machine-assisted processes, we sought to determine whether these standards meet the conditions required for secure and purpose-specific AI implementation. Our research focused on defining AI readiness, operationalizing this concept, and evaluating the extent to which existing standards meet these criteria. AI readiness refers to whether a standard complies with the conditions necessary for ensuring that an AI system operates securely and as intended. To operationalize AI readiness, we developed explicit criteria encompassing AI-specific requirements and the contextual application of these standards. A dual approach involving thorough human analyses and the use of software automation was employed. Human experts annotated standardization documents to create high-quality training data, while machine learning methodologies were utilized to develop AI models capable of classifying the AI readiness of these documents. Three different software tools were developed, to provide a proof-of-concept for a more scalable and efficient review of the 30,000 standards. Despite certain technical and organizational challenges, the integration of both human insight and machine-led processes provided valuable and actionable results and insights for further development. Key findings address the exact choice of words and graphical representation in standardization documents, normative references, categorization of standardization documents, as well as suggestions for concrete document adaptions. The results underscore the importance of an interdisciplinary approach, combining domain-specific knowledge and advanced AI capabilities, to future-proof the intricate regulatory frameworks that underpin our industries and society.