Korean Journal of Artificial Intelligence (한국인공지능학회지)

Korea Artificial Intelligence Association (한국인공지능학회)
권호 표지
DOI QR코드

DOI QR Code

Trends of Artificial Intelligence Product Certification Programs

  • Yejin SHIN (AI Trustworthiness Verification Team, TTA) ;
  • Joon Ho KWAK (AI Trustworthiness Verification Team, TTA) ;
  • KyoungWoo CHO (AI Trustworthiness Verification Team, TTA) ;
  • JaeYoung HWANG (AI Trustworthiness Verification Team, TTA) ;
  • Sung-Min WOO (School of Electrical, Electronics and Communication Engineering, KOREATECH)
  • Received : 2023.04.14
  • Accepted : 2023.09.02
  • Published : 2023.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

With recent advancements in artificial intelligence (AI) technology, more products based on AI are being launched and used. However, using AI safely requires an awareness of the potential risks it can pose. These concerns must be evaluated by experts and users must be informed of the results. In response to this need, many countries have implemented certification programs for products based on AI. In this study, we analyze several trends and differences in AI product certification programs across several countries and emphasize the importance of such programs in ensuring the safety and trustworthiness of products that include AI. To this end, we examine four international AI product certification programs and suggest methods for improving and promoting these programs. The certification programs target AI products produced for specific purposes such as autonomous intelligence systems and facial recognition technology, or extend a conventional software quality certification based on the ISO/IEC 25000 standard. The results of our analysis show that companies aim to strategically differentiate their products in the market by ensuring the quality and trustworthiness of AI technologies. Additionally, we propose methods to improve and promote the certification programs based on the results. These findings provide new knowledge and insights that contribute to the development of AI-based product certification programs.

Keywords

Acknowledgement

This work was supported by the Korean MSIT (Ministry of Science and ICT) as Establishing the foundation of AI Trustworthiness(TTA) & This work was supported by Education and Research promotion program of KOREATECH in 2023.

References

  1. Breck, E., Cai, S., Nielsen, E., Salib, M., & Sculley, D. (2017). The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction. in 2017 IEEE International Conference on Big Data (pp. 1123-1132), December 11-14, Boston, MA, USA, http://dx.doi.org/10.1109/BigData.2017.8258038 
  2. Byun, S. (2019). A Study on the Ethics Certification Program Based on the Morality Types of AI Robots. Journal of Ethics, 1(126), 73-90. http://dx.doi.org/10.15801/je.1.126.201909.73 
  3. Centre for Data Ethics and Innovation (CDEI) (2021). Independent report. The roadmap to an effective AI assurance ecosystem. Retrieved April 13, 2023 (actual access date), from  https://www.gov.uk/government/publications/the-roadmap-to-an-effective-ai-assurance-ecosystem/the-roadmap-to-an-effective-ai-assurance-ecosystem-extended-version. 
  4. Cihon, P., Kleinaltenkamp, M. J., Schuett,J., & Baum, S. D. (2021). AI Certification: Advancing Ethical Practice by Reducing Information Asymmetries. IEEE Transactions on Technology and Society, 2(4), 200-209. https://doi.org/10.48550/arXiv.2105.10356 
  5. Genovesi, S., & Julia, M. M. (2022). Acknowledging Sustainability in the Framework of Ethical Certification for AI. Sustainability, 14(7), 4157. https://doi.org/10.3390/su14074157 
  6. High-Level Expert Group on AI (AI HLEG) (2020). The Assessment List for Trustworthy Artificial Intelligence (ALTAI). European Commission. 
  7. IEEE (2021). IEEE CertifAIEdTM. Retrieved April 13, 2023 from https://engagestandards.ieee.org/ieeecertifaied.html. 
  8. Korean Standards Association (KSA) (2023), AI+ Certification. Retrieved April 13, 2023 from https://www.ksa.or.kr/ksa_kr/6962/subview.do. 
  9. Laboratoire national de metrologie et d'essais (LNE) (2021). Certification of processes for AI. Retrieved April 13, 2023 (actual access date), from https://www.lne.fr/en/service/certification/certification-processes-ai. 
  10. Louradour, S., Madzou, L., & Mella, J. (2020). A Framework for Responsible Limits on Facial Recognition. World Economic Forum, White Paper. 
  11. Machlev, R., Perl, M., Belikov, J., Levy, K. Y., & Levron, Y. (2021). Measuring Explainability and Trustworthiness of Power Quality Disturbances Classifiers Using XAI-Explainable Artificial Intelligence. IEEE Transactions on Industrial Informatics, 18(8), 5127-5137. http://dx.doi.org/10.1109/TII.2021.3126111 
  12. Shin, Y. (2022). 2022 Trustworthy AI Development Guidebook. TTA Journal, 201, 21-27. 
  13. Tao, C., Gao, J., & Wang, T. (2019). Testing and Quality Validation for AI Software-Perspectives, Issues, and Practices. IEEE Access, 7, 120164-120175. http://dx.doi.org/10.1109/ACCESS.2019.2937107