Position Papers of the 20th Conference on Computer Science and Intelligence Systems

DOI: http://dx.doi.org/10.15439/2025F4971

Citation: Position Papers of the 20th Conference on Computer Science and Intelligence Systems, M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 44, pages 23–30 (2025)

Full text

Abstract. Artificial intelligence has recently led to numerous new applications in various industry sectors. Whenever artificial intelligence modules are used in a black-box setting, quality monitoring of such modules remains an open challenge. This implies that users of such modules' cannot predict the modules performance following software updates or retraining. Specifically for regulated devices, keeping track of an artificial intelligence module's behavior and compliance with requirements is crucial. To this end, existing methods for monitoring artificial intelligence modules are investigated and evaluated regarding their practical usability in this paper. Based on the results of the investigation, a proposal for a new adaptive quality monitoring scheme for artificial intelligence modules is made.

References

1. K. McElheran, J. F. Li, E. Brynjolfsson, Z. Kroff, E. Dinlersoz, L. Foster, and N. Zolas, “Ai adoption in america: Who, what, and where,” Journal of Economics & Management Strategy, vol. 33, no. 2, pp. 375–415, 2024.
2. Y. Wei, N. Song, L. Ke, M.-C. Chang, and S. Lyu, “Street object detection / tracking for ai city traffic analysis,” in 2017 IEEE SmartWorld, 2017. https://dx.doi.org/10.1109/UIC-ATC.2017.8397669 pp. 1–5.
3. G. Rani, J. Singh, and A. Khanna, “Comparative analysis of generative ai models,” in 2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT), 2023. https://dx.doi.org/10.1109/ICAICCIT60255.2023.10465941 pp. 760–765.
4. EC, “Regulation (eu) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence,” European Union, Council of the European Union; European Parliament, Directive, February 2024.
5. “WELMEC 7.2 Software Guide,” European cooperation in legal metrology, WELMEC Secretariat, Braunschweig, Standard, Mar. 2022.
6. D. Angluin, “Learning regular sets from queries and counterexamples,” Information and Computation, vol. 75, no. 2, pp. 87–106, 1987. https://dx.doi.org/10.1016/0890-5401(87)90052-6
7. M. Sipser, Introduction to the theory of computation, 2nd ed. Boston, Massachusetts: Thomson, 2006. ISBN 0-534-95097-3
8. S. Windmüller, J. Neubauer, B. Steffen, F. Howar, and O. Bauer, “Active continuous quality control,” in Proceedings of the International Symposium on Component-Based Software Engineering. ACM, Jun. 2013. https://dx.doi.org/10.1145/2465449.2465469 pp. 111–120.
9. J. Neubauer, S. Windmüller, and B. Steffen, “Risk-based testing via active continuous quality control,” International Journal on Software Tools for Technology Transfer, vol. 16, pp. 569–591, 2014. https://dx.doi.org/10.1007/s10009-014-0321-6
10. C. Oliva and L. F. Lago-Fernández, “On the interpretation of recurrent neural networks as finite state machines,” in Proceedings of ICANN 2019: Theoretical Neural Computation: 28th International Conference on Artificial Neural Networks,, vol. I 28. Munich, Germany: Springer International Publishing, September 2019, pp. 312–323.
11. B. Aichernig, E. Muskardin, and A. Pferscher, “Active vs. Passive: A Comparison of Automata Learning Paradigms for Network Protocols,” in Formal Methods for Autonomous Systems and Automated and verifiable Software system development, ser. Electronic Proceedings in Theoretical Computer Science, EPTCS, vol. 371. National ICT Australia Ltd, September 2022. https://dx.doi.org/10.4204/EPTCS.371.1 pp. 1–19.
12. L. C. X. Ho, M. Esche, M. Nischwitz, and S. Glesner, “Black-box conformity tests on regulated measuring instruments: A machine learning approach,” in Proceedings of the IEEE International Instrumentation and Measurement Technology Conference. Chemnitz, Germany: IEEE, 05 2025, to be published.
13. “OIML D 31: General requirements for software controlled measuring instruments,” International Organisation of Legel Metrology, Paris, France, Tech. Rep., 2023.
14. M. Gorrod, The software development lifecycle. London: Palgrave Macmillan UK, 2004, pp. 93–120. ISBN 978-0-230-51029-6. [Online]. Available: https://doi.org/10.1057/9780230510296_4
15. S. Mohseni, N. Zarei, and E. D. Ragan, “A multidisciplinary survey and framework for design and evaluation of explainable ai systems,” ACM Trans. Interact. Intell. Syst., vol. 11, no. 3–4, Sep. 2021. https://dx.doi.org/10.1145/3387166.
16. A. Krizhevsky, “Learning multiple layers of features from tiny images,” University of Toronto, 05 2012.
17. D. Upadhyay, N. Gaikwad, M. Zaman, and S. Sampalli, “Investigating the avalanche effect of various cryptographically secure hash functions and hash-based applications,” IEEE Access, vol. 10, pp. 112 472–112 486, 2022. https://dx.doi.org/10.1109/ACCESS.2022.3215778
18. EC, “Directive 2014/32/EU of the European Parliament and of the Council of 26 February 2014 on the harmonisation of the laws of the Member States relating to the making available on the market of measuring instruments,” European Union, Council of the European Union; European Parliament, Directive, February 2014.