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Is Artificial Intelligence Ready for Standardization?

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Systems, Software and Services Process Improvement (EuroSPI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1251))

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Abstract

Many standards development organizations worldwide work on norms for Artificial Intelligence (AI) technologies and AI related processes. At the same time, many governments and companies massively invest in research on AI. It may be asked if AI research has already produced mature technologies and if this field is ready for standardization. This article looks at today’s situation of AI in the context of needs for standardization. The International Organization for Standardization (ISO) runs a standardization project on AI since 2018. We give an up-to-date overview of the status of this work. While a fully comprehensive survey is not the objective, we describe a number of important aspects of the standardization work in AI. In addition, concrete examples for possible items of AI standards are described and discussed. From a scientific point of view, there are many open research questions that make AI standardization appear to be premature. However, our analysis shows that there is a sound basis for starting to work on AI standardization as being undertaken by ISO and other organizations.

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Notes

  1. 1.

    https://www.iso.org/committee/6794475.html, https://www.iec.ch/dyn/www/f?p=103:7:0::::FSP_ORG_ID:21538.

  2. 2.

    https://www.merriam-webster.com/dictionary/artificial%20intelligence.

  3. 3.

    https://bulkdata.uspto.gov.

  4. 4.

    https://onnx.ai, https://www.khronos.org/nnef.

References

  1. ISO 14155:2011: clinical investigation of medical devices for human subjects - good clinical practice (2011)

    Google Scholar 

  2. ISO/IEC/IEEE 29119–1:2013: software and systems engineering - software testing - part 1:concepts and definitions (2013)

    Google Scholar 

  3. ISO 26262–1:2018: road vehicles - functional safety - part 1: vocabulary (2018)

    Google Scholar 

  4. ISO/IEC 20546:2019: information technology - big data - overview and vocabulary (2019)

    Google Scholar 

  5. ISO/IEC WD 15938–17: multimedia content description interface - part 17: compression of neural networks for multimedia content description and analysis (2020)

    Google Scholar 

  6. Ang, J.C., Mirzal, A., Haron, H., Hamed, H.N.A.: Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection. IEEE/ACM Trans. Comput. Biol. Bioinf. 13(5), 971–989 (2016)

    Article  Google Scholar 

  7. Bastani, O., Ioannou, Y., Lampropoulos, L., Vytiniotis, D., Nori, A.V., Criminisi, A.: Measuring neural net robustness with constraints. In: Proceedings of 30th International Conference on on Neural Information Processing Systems, pp. 2621–2629. Curran Associates Inc. (2016)

    Google Scholar 

  8. Becker, M., Lippel, J., Stuhlsatz, A., Zielke, T.: Robust dimensionality reduction for data visualization with deep neural networks. Graph. Models 108, 101060 (2020). https://doi.org/10.1016/j.gmod.2020.101060

    Article  Google Scholar 

  9. Belkin, M., Hsu, D., Ma, S., Mandal, S.: Reconciling modern machine-learning practice and the classical bias-variance trade-off. Proc. Natl. Acad. Sci. 116(32), 15849–15854 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  10. Blind, K., Jungmittag, A., Mangelsdorf, A.: The economic benefits of standardisation. An update of the study carried out by DIN in 2000. DIN Berlin, January 2012

    Google Scholar 

  11. BSI: An investigation into the performance of facial recognition systems relative to their planned use in photo identification documents - BioP I. Technical report, Bundesamt für Sicherheit in der Informationstechnik (BSI), Bundeskriminalamt (BKA), secunet AG, April 2004. https://www.bsi.bund.de/SharedDocs/Downloads/EN/BSI/Publications/Studies/BioP/BioPfinalreport_pdf.pdf

  12. Burke, J., Dunne, B.: Field testing of six decision support systems for scheduling fungicide applications to control mycosphaerella graminicola on winter wheat crops in Ireland. J. Agric. Sci. 146(04), 415 (2008)

    Article  Google Scholar 

  13. Cunningham, S., et al.: Software testing: a changing career. In: Walker, A., O’Connor, R.V., Messnarz, R. (eds.) EuroSPI 2019. CCIS, vol. 1060, pp. 731–742. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28005-5_57

    Chapter  Google Scholar 

  14. Dietterich, T.G.: Steps toward robust artificial intelligence. AI Mag. 38(3), 3–24 (2017)

    Article  Google Scholar 

  15. DIN: Interdisciplinary DIN working committee “artificial intelligence” (2018). https://www.din.de/en/innovation-and-research/artificial-intelligence/ai-working-committee

  16. Dong, X., Yu, Z., Cao, W., Shi, Y., Ma, Q.: A survey on ensemble learning. Front. Comput. Sci. 14(2), 241–258 (2019). https://doi.org/10.1007/s11704-019-8208-z

    Article  Google Scholar 

  17. Duthon, P., Bernardin, F., Chausse, F., Colomb, M.: Benchmark for the robustness of image features in rainy conditions. Mach. Vis. Appl. 29(5), 915–927 (2018). https://doi.org/10.1007/s00138-018-0945-8

    Article  Google Scholar 

  18. EU: Funding for AI. https://ec.europa.eu/info/research-and-innovation/research-area/industrial-research-and-innovation/key-enabling-technologies/artificial-intelligence-ai_en

  19. Feldman, J.A., Ballard, D.H.: Connectionist models and their properties. Cogn. Sci. 6(3), 205–254 (1982). https://doi.org/10.1207/s15516709cog0603_1

    Article  Google Scholar 

  20. Flasinski, M.: Introduction to Artificial Intelligence. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-319-40022-8

    Book  MATH  Google Scholar 

  21. Floridi, L.: AI and its new winter: from myths to realities. Philos. Technol. 33(1), 1–3 (2020). https://doi.org/10.1007/s13347-020-00396-6

    Article  Google Scholar 

  22. Floridi, L., et al.: AI4people–an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds Mach. 28(4), 689–707 (2018)

    Article  Google Scholar 

  23. Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. 46(4), 1–37 (2014)

    Article  MATH  Google Scholar 

  24. Ghahramani, Z.: Probabilistic machine learning and artificial intelligence. Nature 521(7553), 452–459 (2015). https://doi.org/10.1038/nature14541

    Article  Google Scholar 

  25. Goebel, R., et al.: Explainable AI: the new 42? In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2018. LNCS, vol. 11015, pp. 295–303. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99740-7_21

    Chapter  Google Scholar 

  26. Guidotti, D.: Enhancing neural networks through formal verification. In: Alviano, M., Greco, G., Maratea, M., Scarcello, F. (eds.) Discussion and Doctoral Consortium papers of AI*IA 2019–18th International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, 19–22 November 2019. CEUR Workshop Proceedings, vol. 2495, pp. 107–112. CEUR-WS.org (2019)

    Google Scholar 

  27. Hatani, F.: Artificial intelligence in Japan: policy, prospects, and obstacles in the automotive industry. In: Khare, A., Ishikura, H., Baber, W.W. (eds.) Transforming Japanese Business. FBF, pp. 211–226. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0327-6_15

    Chapter  Google Scholar 

  28. Hayes-Roth, F., Jacobstein, N.: The state of knowledge-based systems. Commun. ACM 37(3), 26–39 (1994)

    Article  Google Scholar 

  29. Hendrycks, D., Dietterich, T.G.: Benchmarking neural network robustness to common corruptions and perturbations. In: 7th International Conference on Learning Representations, ICLR 2019. OpenReview.net, New Orleans, May 2019

    Google Scholar 

  30. Huang, X., et al.: A survey of safety and trustworthiness of deep neural networks. arXiv preprint arXiv:1812.08342 (2018)

  31. Hurlburt, G.: How much to trust artificial intelligence? IT Prof. 19(4), 7–11 (2017). https://doi.org/10.1109/MITP.2017.3051326

    Article  Google Scholar 

  32. Iversen, E.J., Vedel, T., Werle, R.: Standardization and the democratic design of information and communication technology. Knowl. Technol. Policy 17(2), 104–126 (2004). https://doi.org/10.1007/s12130-004-1027-y

    Article  Google Scholar 

  33. Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)

    Article  Google Scholar 

  34. Lamel, L., et al.: Field trials of a telephone service for rail travel information. In: Proceedings of of IVTTA 1996. Workshop on Interactive Voice Technology for Telecommunications Applications, pp. 111–116. IEEE (1996)

    Google Scholar 

  35. Liang, H., Fu, W., Yi, F.: A survey of recent advances in transfer learning. In: 19th IEEE International Conference on Communication Technology, ICCT 2019. pp. 1516–1523. IEEE, Xi’an, October 2019

    Google Scholar 

  36. Louridas, P., Ebert, C.: Machine learning. IEEE Softw. 33(5), 110–115 (2016)

    Article  Google Scholar 

  37. Moor, J.: The dartmouth college artificial intelligence conference: the next fifty years. AI Mag. 27(4), 87–87 (2006)

    Google Scholar 

  38. Nilsson, N.J.: Principles of Artificial Intelligence. Symbolic Computation. Springer, Heidelberg (1982)

    Book  MATH  Google Scholar 

  39. Numan, G.: Testing artificial intelligence. In: Goericke, S., et al. (eds.) The Future of Software Quality Assurance, pp. 123–136. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-29509-7_10

    Chapter  Google Scholar 

  40. O’Sullivan, E., Brévignon-Dodin, L.: Role of standardisation in support of emerging technologies. Technical report, Institute for Manufacturing, University of Cambridge, June 2012

    Google Scholar 

  41. Poth, A., Beck, Q., Riel, A.: Artificial intelligence helps making quality assurance processes leaner. In: Walker, A., O’Connor, R.V., Messnarz, R. (eds.) EuroSPI 2019. CCIS, vol. 1060, pp. 722–730. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28005-5_56

    Chapter  Google Scholar 

  42. Rao, V.R.: How data becomes knowledge, part 1: from data to knowledge, March 2018. https://www.ibm.com/developerworks/library/ba-data-becomes-knowledge-1/index.html

  43. Rosenblatt, F.: The perceptron: A probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386 (1958)

    Article  Google Scholar 

  44. Rozenblit, J.W.: Cognitive computing: principles, architectures, and applications. In: Proceedings of 19th European Conference on Modelling and Simulation (ECMS) (2005)

    Google Scholar 

  45. Salay, R., Queiroz, R., Czarnecki, K.: An analysis of ISO 26262: using machine learning safely in automotive software. CoRR abs/1709.02435 (2017)

    Google Scholar 

  46. Santos, I.C., Gazelle, G.S., Rocha, L.A., Tavares, J.M.R.: Medical device specificities: opportunities for a dedicated product development methodology. Expert Rev. Med. Devices 9(3), 299–311 (2012)

    Article  Google Scholar 

  47. SC1: ISO/IEC 2382–31:1997(en) information technology - vocabulary - part 31: Artificial intelligence - machine learning (1997)

    Google Scholar 

  48. SC42 WG1: Artificial intelligence concepts and terminology. Technical report CD 22989, ISO/IEC JTC 1/SC 42 Artificial Intelligence (2019)

    Google Scholar 

  49. SC42 WG1: Framework for artificial intelligence (AI) systems using machine learning (ML). Technical report CD 23053, ISO/IEC JTC 1/SC 42 Artificial Intelligence (2019)

    Google Scholar 

  50. SC42 WG3: Assessment of the robustness of neural networks - part 1: overview. Technical report CD TR 24029–1, ISO/IEC JTC 1/SC 42 Artificial Intelligence (2019)

    Google Scholar 

  51. SC42 WG3: Bias in AI systems and AI aided decision making. Technical report AWI TR 24027, ISO/IEC JTC 1/SC 42 Artificial Intelligence (2020)

    Google Scholar 

  52. SC42 WG3: Overview of ethical and societal concerns. Technical report AWI TR 24368, ISO/IEC JTC 1/SC 42 Artificial Intelligence (2020)

    Google Scholar 

  53. SC42 WG3: Overview of trustworthiness in artificial intelligence. Technical report PRF TR 24028, ISO/IEC JTC 1/SC 42 Artificial Intelligence (2020)

    Google Scholar 

  54. SC42 WG4: Use cases and applications. Technical report CD TR 24030, ISO/IEC JTC 1/SC 42 Artificial Intelligence (2019)

    Google Scholar 

  55. Smolensky, P.: Connectionist AI, symbolic AI, and the brain. Artif. Intell. Rev. 1(2), 95–109 (1987). https://doi.org/10.1007/BF00130011

    Article  Google Scholar 

  56. Stone, P., et al.: Artificial intelligence and life in 2030. Technical report, Stanford University, September 2016

    Google Scholar 

  57. Stuhlsatz, A., Lippel, J., Zielke, T.: Feature extraction with deep neural networks by a generalized discriminant analysis. IEEE Trans. Neural Netw. Learn. Syst. 23(4), 596–608 (2012)

    Article  Google Scholar 

  58. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. The MIT Press, Cambridge (2018)

    MATH  Google Scholar 

  59. Tassey, G.: Standardization in technology-based markets. Res. Policy 29(4–5), 587–602 (2000). https://doi.org/10.1016/s0048-7333(99)00091-8

    Article  Google Scholar 

  60. Turing, A.M.: Computing machinery and intelligence. Mind LIX 59(236), 433–460 (1950). https://doi.org/10.1093/mind/lix.236.433

    Article  MathSciNet  Google Scholar 

  61. UK-Government: The pathway to driverless cars: a code of practice for testing. Technical report, Department for Transport, Great Minster House, 33 Horseferry Road, London (2015). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/446316/pathway-driverless-cars.pdf

  62. Wang, M., Deng, W.: Deep face recognition: a survey. CoRR abs/1804.06655 (2018)

    Google Scholar 

  63. Weiss, H.: IBM verwettet seine zukunft auf cognitive computing. Computerwoche, October 2015. https://www.computerwoche.de/a/ibm-verwettet-seine-zukunft-auf-cognitive-computing,3218187

  64. Yu, B., Kumbier, K.: Artificial intelligence and statistics. Front. Inf. Technol. Electron. Eng. 19(1), 6–9 (2018)

    Article  Google Scholar 

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Acknowledgements

We would like to thank Dominic Kalkbrenner and Jens Lippel for programming the data analytics on the bulk data from the US patent office. Dr. Andreas Riel provided valuable feedback and discussions on the structure of the paper and the relevance for the automotive industry.

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Zielke, T. (2020). Is Artificial Intelligence Ready for Standardization?. In: Yilmaz, M., Niemann, J., Clarke, P., Messnarz, R. (eds) Systems, Software and Services Process Improvement. EuroSPI 2020. Communications in Computer and Information Science, vol 1251. Springer, Cham. https://doi.org/10.1007/978-3-030-56441-4_19

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