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

Author Adrian Seeliger



PDF
Thumbnail PDF

File

OASIcs.SAIA.2024.8.pdf
  • Filesize: 376 kB
  • 6 pages

Document Identifiers

Author Details

Adrian Seeliger
  • Deutsches Institut für Normung e.V. (DIN), Berlin, Germany

Acknowledgements

We extend our gratitude to the Bundesministerium für Wirtschaft und Klimaschutz (BWMK). Our sincere thanks for their invaluable support and guidance throughout this project to the Fraunhofer IAIS Team "AI Safeguarding and Certification" and Team "Natural Language Understanding" for their expertise and collaboration. We also wish to thank Fraunhofer IKS, IEM, HHI, MEWIS, and INT for their significant contributions without which this project would not have been possible.

Cite As Get BibTex

Adrian Seeliger. AI Readiness of Standards: Bridging Traditional Norms with Modern Technologies (Practitioner Track). In Symposium on Scaling AI Assessments (SAIA 2024). Open Access Series in Informatics (OASIcs), Volume 126, pp. 8:1-8:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/OASIcs.SAIA.2024.8

Abstract

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.

Subject Classification

ACM Subject Classification
  • Proper nouns: People, technologies and companies → International Organization for Standardization
  • Computing methodologies → Artificial intelligence
  • Proper nouns: People, technologies and companies → European Telecommunications Standards Institute
Keywords
  • Standardization
  • Norms and Standards
  • AI Readiness
  • Artificial Intelligence
  • Knowledge Automation

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Wajid Ali and Abdul Zahid Khan. Factors influencing readiness for artificial intelligence: a systematic literature review. Data Science and Management, 2024. Google Scholar
  2. Sulaiman Alsheibani, Yen Cheung, and Chris Messom. Artificial intelligence adoption: Ai-readiness at firm-level. In Pacific Asia Conference on Information Systems 2018, page 37. Association for Information Systems, 2018. URL: https://aisel.aisnet.org/pacis2018/37.
  3. Jonny Holmström. From ai to digital transformation: The ai readiness framework. Business Horizons, 65(3):329-339, 2022. URL: https://doi.org/10.1016/j.bushor.2021.03.006.
  4. Jan Jöhnk, Malte Weißert, and Katrin Wyrtki. Ready or not, ai comes—an interview study of organizational ai readiness factors. Business & Information Systems Engineering, 63(1):5-20, 2021. URL: https://doi.org/10.1007/S12599-020-00676-7.
  5. Kostina Prifti, Esra Demir, Julia Krämer, Klaus Heine, and Evert Stamhuis, editors. Digital Governance: Confronting the Challenges Posed by Artificial Intelligence. Information Technology and Law Series. T.M.C. Asser Press The Hague, 1 edition, 2024. Hardcover due: 08 January 2025, Softcover due: 08 January 2026, eBook due: 08 January 2025. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail