Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Experimental Characterisation of Tool Hardness Evolution Under Consideration of Process Relevant Cyclic Thermal and Mechanical Loading During Industrial Forging Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Sustainable Interaction of Human and Artificial Intelligence in Cyber Production Management Systems

Authors : P. Burggräf, J. Wagner, T. M. Saßmannshausen

Published in: Production at the leading edge of technology

Publisher: Springer Berlin Heidelberg

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

AI will increasingly take over complex cognitive tasks and support human thinking and thus change the system of production management over decades to a cyber production management system. It has to be considered that AI can behave proactively, unexpectedly and incomprehensibly for humans. Here the human factor trust is essential and even becomes more relevant to determine sustainable relationship between humans and AI.
This leads to the research question at the edge of production research: What does human trust in an AI assistant depend, on in production management decisions? To answer this question this article statistically examines a set of previously identified influencing factors on human trust. From these results an explanatory model is derived, which serves as a first design guideline for a socially sustainable human-AI interaction in production management.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Process Monitoring Using Machine Learning for Semi-Automatic Drilling of Rivet Holes in the Aerospace Industry
next chapter Autoconfiguration of a Vibration-Based Anomaly Detection System with Sparse a-priori Knowledge Using Autoencoder Networks
Footnotes
1
First, the dependent variable Trust is not normally distributed, according to Shapiro–Wilk’s test (p-value < 2.2e−16, should be > 5%) and optical inspection of density and QQ plot. Second, homoscedasticity is just met as per Fligner-Killeen test (p = 0.8, should be > 5%). Third, the linearity is proofed by the correlation matrix (using Spearman Rho), where all main effects show significant correlations (p-value < 5%).
 
Literature
1.
Kuhnle, A., Röhrig, N., Lanza, G.: Autonomous order dispatching in the semiconductor industry using reinforcement learning. Procedia CIRP 79, 391–396 (2019)CrossRef
2.
Dannapfel, M., et al.: Human machine cooperation in smart production: evaluation of the organizational readiness. Int. J. Mech. Eng. Robot. Res. 8(2), 327–332 (2019)CrossRef
3.
McAfee, A., Brynjolfsson, E.: Machine, Platform. Crowd. W.W. Norton & Company, New York (2017)
4.
Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)CrossRef
5.
Moravčík, M., et al.: DeepStack: expert-level artificial intelligence in heads-up no-limit poker. Science 356(6337), 508–513 (2017)MathSciNetMATHCrossRef
6.
Dellermann, D., et al.: Hybrid intelligence. Bus. Inform. Syst. Eng. 61(5), 637–643 (2019)CrossRef
7.
Russell, S.J., Norvig, P.: Artificial Intelligence, 3rd edn. Pearson Education, London (2016)MATH
8.
World Economic Forum: Deep Shift: Technology Tipping Points and Societal Impact. World Economic Forum, Geneva (2015)
9.
Burggräf, P., Wagner, J., Koke, B.: Artificial intelligence in production management. In: International Conference on Information Management and Processing (ICIMP), pp. 82–88. IEEE, London (2018)
10.
Kuhnle, A., et al.: Intelligente Produktionsplanung und -steuerung. wt Werkstattstechnik online 107(9), 625–629 (2017)
11.
Burggräf, P., et al.: Adaptive remanufacturing for lifecycle optimization of connected production resources. Procedia CIRP 90, 61–66 (2020)CrossRef
12.
Gabel, T., Riedmiller, M.: Adaptive reactive job-shop scheduling with reinforcement learning agents. Int. J. Inf. Technol. Intel. Comput. 24(4), 14–18 (2008)
13.
Qu, S., et al.: Optimized adaptive scheduling of a manufacturing process system with multi-skill workforce and multiple machine types. Procedia CIRP 57, 55–60 (2016)CrossRef
14.
Burggräf, P., et al.: Performance assessment methodology for AI-supported decision-making in production management. Procedia CIRP (2020)
15.
16.
17.
18.
19.
20.
Ranz, F., Hummel, V., Sihn, W.: Capability-based task allocation in human-robot collaboration. Procedia Manuf. 9, 182–189 (2017)CrossRef
21.
von Foerster, H.: KybernEthik. Merve-Verl., Berlin (1993)
22.
Windischer, A.: Kooperatives Planen. Universität Zürich, Zürich (2003)
23.
Platte, L., Schönefeld, K., Hess, F.: Künstliche Intelligenz in der Produktionstechnik – eine Kränkung des Ingenieurs? FERRUM 91, 94–101 (2019)
24.
Saßmannshausen, T.M.: Vertrauen in Entscheidungen künstlicher Intelligenz im Produktionsmanagement. Shaker, Düren (2019)
25.
Hancock, P.A., Stowers, K.L., Kessler, T.T.: Can we trust autonomous systems? In: Ayaz, H., Dehais, F. (eds.) Neuroergonomics, p. 199. Academic Press, London (2019)CrossRef
26.
de Visser, E.J., Pak, R., Shaw, T.H.: From ‘automation’ to ‘autonomy’: the importance of trust repair in human-machine interaction. Ergonomics 61(10), 1409–1427 (2018)CrossRef
27.
28.
Hancock, P.A.: Imposing limits on autonomous systems. Ergonomics 60(2), 284–291 (2017)CrossRef
29.
30.
Hancock, P.A.: Automation: how much is too much? Ergonomics 57(3), 449–454 (2013)CrossRef
31.
Rousseau, D.M., et al.: Not so different after all: a cross-discipline view of trust. AMR 23(3), 393–404 (1998)CrossRef
32.
Mayer, R.C., Davis, J.H., Schoorman, F.D.: An integrative model of organizational trust. Acad. Manag. Rev. 20(3), 709–734 (1995)CrossRef
33.
34.
Lee, J.D., See, K.A.: Trust in automation: designing for appropriate reliance. Hum. Factors 46(1), 50–80 (2004)CrossRef
35.
Söllner, M., et al.: Understanding the formation of trust in IT artifacts. In: Proceedings of the International Conference on Information Systems (ICIS), pp. 1–18. Orlando Florida (2012)
36.
Hancock, P.A., Billings, D.R., Schaefer, K.E.: Can you trust your robot? Ergon. Des. 19(3), 24–29 (2011)
37.
Hengstler, M., Enkel, E., Duelli, S.: Applied artificial intelligence and trust—the case of autonomous vehicles and medical assistance devices. Technol. Forecast. Soc. Chang. 105, 105–120 (2016)CrossRef
38.
Parasuraman, R., Riley, V.: Humans and automation: use, misuse, disuse. Abuse. Hum. Factors 39(2), 230–253 (1997)CrossRef
39.
Drnec, K., et al.: From trust in automation to decision neuroscience. Front. Hum. Neurosci. 10, 290 (2016)CrossRef
40.
Mayring, P.: Qualitative Content Analysis: Theoretical Foundation, Basic Procedures and Soft-Ware Solution. Gesis, Klagenfurt (2014)
41.
Siau, K., Wang, W.: Building trust in artificial intelligence, machine learning, and robotics. Cutter Bus. Technol. J. 31(2), 47–53 (2018)
42.
Schaefer, K.E., et al.: A meta-analysis of factors influencing the development of trust in automation. Hum. Factors 58(3), 377–400 (2016)CrossRef
43.
Hancock, P.A., et al.: A meta-analysis of factors affecting trust in human-robot interaction. Hum. Factors 53(5), 517–527 (2011)CrossRef
44.
45.
Schreiber, J.B., et al.: Reporting structural equation modeling and confirmatory factor analysis results. J. Edu. Res. 99(6), 323–338 (2006)CrossRef
46.
Hair, J.F., et al.: Multivariate Data Analysis, 7th edn. Pearson, Harlow (2014)
47.
Susanti, Y., et al.: M estimation, S estimation, and MM estimation in robust regression. Int. J. Pure Appl. Mathemat. 91(3), 349–360 (2014)MATH
48.
Wilcox, R.R., Keselman, H.J.: Modern regression methods that can substantially increase power and provide a more accurate understanding of associations. Eur. J. Pers. 26(3), 165–174 (2012)CrossRef
49.
50.
51.
52.
53.
Matthews, G., et al.: Individual differences in trust in autonomous robots. In: IEEE Transactions on Human-Machine System (early access), 1–11 (2019)
Metadata
Title
Sustainable Interaction of Human and Artificial Intelligence in Cyber Production Management Systems
Authors
P. Burggräf
J. Wagner
T. M. Saßmannshausen
Copyright Year
2021
Publisher
Springer Berlin Heidelberg
Book
Production at the leading edge of technology

Print ISBN: 978-3-662-62137-0

Electronic ISBN: 978-3-662-62138-7

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-662-62138-7_51

Premium Partners

    Image Credits