Human in the AI Loop in Production Environments | SpringerLink
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Abstract

The integration of Artificial Intelligence (AI) in manufacturing is often pursued as technology push. In contrast, this paper looks upon the AI-human interaction from a viewpoint that considers both to play an important role in reshaping their individual capabilities. It specifically focuses on how humans can play an important role in enhancing AI capabilities. The introduced concepts are tested in an industrial case study of vision-based inspection in production lines. Furthermore, the paper highlights the need to consider relevant implications for work design for AI integration. The contribution can be of practical value for system developers and work designers in how to target at the design stage the human contribution in AI-enabled systems for production environments.

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References

  1. DOD: Manpower, personnel, training, and safety (MPTS) in the defense system acquisition process. DoD Directive 5000.53, Washington, DC (1988)

    Google Scholar 

  2. Caroly, S., Barcellini, F.: A conceptual framework of collective activity in constructive ergonomics. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds.) IEA 2018. AISC, vol. 822, pp. 658–664. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96077-7_71

    Chapter  Google Scholar 

  3. Burggräf, P., Wagner, J., Saßmannshausen, T.M.: Sustainable interaction of human and artificial intelligence in cyber production management systems. In: Behrens, B.-A., Brosius, A., Hintze, W., Ihlenfeldt, S., Wulfsberg, J.J. (eds.) WGP 2020. LNPE, pp. 508–517. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3-662-62138-7_51

    Chapter  Google Scholar 

  4. Raisch, S., Krakowski, S.: Artificial intelligence and management: the automation–augmentation paradox. Acad. Manage. Rev. 46, 192–210 (2021). https://doi.org/10.5465/AMR.2018.0072

    Article  Google Scholar 

  5. Grønsund, T., Aanestad, M.: Augmenting the algorithm: emerging human-in-the-loop work configurations. J. Strateg. Inf. Syst. 29, 101614 (2020). https://doi.org/10.1016/j.jsis.2020.101614

    Article  Google Scholar 

  6. Emmanouilidis, C., et al.: Enabling the human in the loop: linked data and knowledge in industrial cyber-physical systems. Annu. Rev. Control. 47, 249–265 (2019). https://doi.org/10.1016/j.arcontrol.2019.03.004

    Article  Google Scholar 

  7. Langley, P., Laird, J.E., Rogers, S.: Cognitive architectures: research issues and challenges. Cogn. Syst. Res. 10, 141–160 (2009). https://doi.org/10.1016/j.cogsys.2006.07.004

    Article  Google Scholar 

  8. Kaptelinin, V., Nardi, B.: Affordances in HCI: toward a mediated action perspective. In: CHI ’12: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Austin, Texas, USA, pp. 967–976 (2012). https://doi.org/10.1145/2207676.2208541

  9. Neumann, W.P., Winkelhaus, S., Grosse, E.H., Glock, C.H.: Industry 4.0 and the human factor – a systems framework and analysis methodology for successful development. Int. J. Prod. Econ. 233, 107992 (2021). https://doi.org/10.1016/j.ijpe.2020.107992

    Article  Google Scholar 

  10. Parker, S.K., Grote, G.: Automation, algorithms, and beyond: why work design matters more than ever in a digital world. Appl. Psychol. (2020). https://doi.org/10.1111/apps.12241

  11. Cimini, C., Pirola, F., Pinto, R., Cavalieri, S.: A human-in-the-loop manufacturing control architecture for the next generation of production systems. J. Manufact. Syst. 54, 258–271 (2020). https://doi.org/10.1016/j.jmsy.2020.01.002

    Article  Google Scholar 

  12. Russel, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson, New York (2020)

    Google Scholar 

  13. Raisch, S., Krakowski, S.: Artificial intelligence and management: the automation-augmentation paradox. Acad. Manage. Rev. 46(1), 192–210 (2020). https://doi.org/10.5465/2018.0072

    Article  Google Scholar 

  14. Kadir, B.A., Broberg, O.: Human-centered design of work systems in the transition to industry 4.0. Appl. Ergon. 92, 103334 (2021). https://doi.org/10.1016/j.apergo.2020.103334

    Article  Google Scholar 

  15. Romero, D., et al.: Towards an operator 4.0 typology: a human-centric perspective on the fourth industrial revolution technologies. In: CIE 2016: 46th International Conferences on Computers and Industrial Engineering, Tianjin (2016)

    Google Scholar 

  16. Raisamo, R., Rakkolainen, I., Majaranta, P., Salminen, K., Rantala, J., Farooq, A.: Human augmentation: past, present and future. Int. J. Hum. Comput. Stud. 131, 131–143 (2019). https://doi.org/10.1016/j.ijhcs.2019.05.008

    Article  Google Scholar 

  17. Lampe, M., Strassner, M., Fleisch, E.: A Ubiquitous computing environment for aircraft maintenance. In: Proceedings of the 2004 ACM Symposium on Applied Computing - SAC 2004, p. 1586 (2004). https://doi.org/10.1145/967900.968217

  18. Washburn, C., Stringfellow, P., Gramopadhye, A.: Using multimodal technologies to enhance aviation maintenance inspection training. In: Duffy, V.G. (ed.) ICDHM 2007. LNCS, vol. 4561, pp. 1018–1026. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73321-8_114

    Chapter  Google Scholar 

  19. Schwald, B., DeLaval, B.: An augmented aeality system for training and assistance to maintenance in the industrial context. In: WSCG 2003, International Conference in Cent. Europe Comput. Graph., Vis. Comput. Vision, pp. 425–432 (2003). https://doi.org/10.1007/11941354_29

  20. Li, J.R., Khoo, L.P., Tor, S.B.: Desktop virtual reality for maintenance training: an object oriented prototype system (V-REALISM). Comput. Ind. 52, 109–125 (2003). https://doi.org/10.1016/S0166-3615(03)00103-9

    Article  Google Scholar 

  21. Papathanasiou, N., Karampatzakis, D., Koulouriotis, D., Emmanouilidis, C.: Mobile personalised support in industrial environments: coupling learning with context - aware features. In: Grabot, B., Vallespir, B., Gomes, S., Bouras, A., Kiritsis, D. (eds.) APMS 2014. IAICT, vol. 438, pp. 298–306. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44739-0_37

    Chapter  Google Scholar 

  22. Fox, S., Aranko, O., Heilala, J., Vahala, P.: Exoskeletons: comprehensive, comparative and critical analyses of their potential to improve manufacturing performance. J. Manuf. Technol. Manage. 31, 1261–1280 (2019). https://doi.org/10.1108/JMTM-01-2019-0023

    Article  Google Scholar 

  23. Goose, S., Sudarsky, S., Zhang, X., Navab, N.: Speech-enabled augmented reality supporting mobile industrial maintenance. IEEE Pervasive Comput. 2, 65–70 (2003). https://doi.org/10.1109/MPRV.2003.1186727

    Article  Google Scholar 

  24. Zhang, B., Wang, J., Fuhlbrigge, T.: A review of the commercial brain-computer interface technology from perspective of industrial robotics. In: 2010 IEEE International Conference on Automation and Logistics, pp. 379–384 (2010). https://doi.org/10.1109/ICAL.2010.5585311

  25. Somerville, I.: Software Engineering. Pearson, Harlow (2016)

    Google Scholar 

  26. Zhuang, F., et al.: A comprehensive survey on transfer learning. Proc. IEEE. 109, 43–76 (2021). https://doi.org/10.1109/JPROC.2020.3004555

    Article  Google Scholar 

  27. Deng, C., Ji, X., Rainey, C., Zhang, J., Lu, W.: Integrating machine learning with human knowledge. iScience 23, 101656 (2020). https://doi.org/10.1016/j.isci.2020.101656

    Article  Google Scholar 

  28. James Wilson, H., Daugherty, P.R.: Collaborative intelligence: humans and AI are joining forces. Harv. Bus. Rev. 96(4), 114–123 (2018)

    Google Scholar 

  29. Lyytinen, K., Nickerson, J.V, King, J.L.: Metahuman systems = humans + machines that learn. J. Inf. Technol., 0268396220915917 (2020). https://doi.org/10.1177/0268396220915917

  30. LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. In: The Handbook of Brain Theory and Neural Networks, no. 10, p. 3361 (1995)

    Google Scholar 

  31. Monarch, M.: No TitleHuman-in-the-Loop Machine Learning. Manning (2021)

    Google Scholar 

  32. Kolus, A., Wells, R., Neumann, P.: Production quality and human factors engineering: a systematic review and theoretical framework. Appl. Ergon. 73, 55–89 (2018). https://doi.org/10.1016/j.apergo.2018.05.010

    Article  Google Scholar 

  33. Oldham, G.R., Richard Hackman, J.: Not what it was and not what it will be: the future of job design research. J. Organ. Behav. 31, 463–479 (2010). https://doi.org/10.1002/job.678

    Article  Google Scholar 

  34. Morgeson, F.P., Humphrey, S.E.: Job and team design: toward a more integrative conceptualization of work design. Res. Pers. Hum. Resour. Manage. 27, 39–91 (2008). https://doi.org/10.1016/S0742-7301(08)27002-7

    Article  Google Scholar 

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Acknowledgements

The research was supported through H2020 grant ID 956573. Sourcing the image data in the project through Philips Consumer Lifestyle B.V. is gratefully acknowledged.

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Correspondence to C. Emmanouilidis .

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Emmanouilidis, C., Waschull, S., Bokhorst, J.A.C., Wortmann, J.C. (2021). Human in the AI Loop in Production Environments. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 633. Springer, Cham. https://doi.org/10.1007/978-3-030-85910-7_35

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  • DOI: https://doi.org/10.1007/978-3-030-85910-7_35

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