IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v66y2021ics0160791x21001226.html
   My bibliography  Save this article

An alliance of humans and machines for machine learning: Hybrid intelligent systems and their design principles

Author

Listed:
  • Ostheimer, Julia
  • Chowdhury, Soumitra
  • Iqbal, Sarfraz

Abstract

With the growing number of applications of artificial intelligence such as autonomous cars or smart industrial equipment, the inaccuracy of utilized machine learning algorithms could lead to catastrophic outcomes. Human-in-the-loop computing combines human and machine intelligence resulting in a hybrid intelligence of complementary strengths. Whereas machines are unbeatable in logic and computation speed, humans are contributing with their creative and dynamic minds. Hybrid intelligent systems are necessary to achieve high accuracy and reliability of machine learning algorithms. In a design science research project with a Swedish manufacturing company, this paper presents an application of human-in-the-loop computing to make operational processes more efficient. While conceptualizing a Smart Power Distribution for electric industrial equipment, this research presents a set of principles to design machine-learning algorithms for hybrid intelligence. From being AI-ready as an organization to clearly focusing on the customer benefits of a hybrid intelligent system, designers need to build and strengthen the trust in the human-AI relationship to make future applications successful and reliable. With the growing trends of technological advancements and incorporation of artificial intelligence in more and more applications, the alliance of humans and machines have become even more crucial.

Suggested Citation

  • Ostheimer, Julia & Chowdhury, Soumitra & Iqbal, Sarfraz, 2021. "An alliance of humans and machines for machine learning: Hybrid intelligent systems and their design principles," Technology in Society, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:teinso:v:66:y:2021:i:c:s0160791x21001226
    DOI: 10.1016/j.techsoc.2021.101647
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X21001226
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techsoc.2021.101647?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Naveed, Kashif & Watanabe, Chihiro & Neittaanmäki, Pekka, 2017. "Co-evolution between streaming and live music leads a way to the sustainable growth of music industry – Lessons from the US experiences," Technology in Society, Elsevier, vol. 50(C), pages 1-19.
    2. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    3. Al-Emran, Mostafa & Mezhuyev, Vitaliy & Kamaludin, Adzhar, 2020. "Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance," Technology in Society, Elsevier, vol. 61(C).
    4. Sulin Ba & Jan Stallaert & Andrew B. Whinston, 2001. "Research Commentary: Introducing a Third Dimension in Information Systems Design—The Case for Incentive Alignment," Information Systems Research, INFORMS, vol. 12(3), pages 225-239, September.
    5. Coccia, Mario, 2020. "Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence," Technology in Society, Elsevier, vol. 60(C).
    6. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    7. Siyam, Nur & Alqaryouti, Omar & Abdallah, Sherief, 2020. "Mining government tweets to identify and predict citizens engagement," Technology in Society, Elsevier, vol. 60(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Lixuan & Yencha, Christopher, 2022. "Examining perceptions towards hiring algorithms," Technology in Society, Elsevier, vol. 68(C).
    2. Chris Turner & John Oyekan & Wolfgang Garn & Cian Duggan & Khaled Abdou, 2022. "Industry 5.0 and the Circular Economy: Utilizing LCA with Intelligent Products," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    3. Zhang, Weidong & Zuo, Na & He, Wu & Li, Songtao & Yu, Lu, 2021. "Factors influencing the use of artificial intelligence in government: Evidence from China," Technology in Society, Elsevier, vol. 66(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Trivedi, Shrawan Kumar, 2020. "A study on credit scoring modeling with different feature selection and machine learning approaches," Technology in Society, Elsevier, vol. 63(C).
    2. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
    3. Jermain C. Kaminski & Christian Hopp, 2020. "Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals," Small Business Economics, Springer, vol. 55(3), pages 627-649, October.
    4. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    5. Yuchen Zhang & Wei Yang, 2022. "Breakthrough invention and problem complexity: Evidence from a quasi‐experiment," Strategic Management Journal, Wiley Blackwell, vol. 43(12), pages 2510-2544, December.
    6. Naveed, Kashif & Watanabe, Chihiro & Neittaanmäki, Pekka, 2018. "The transformative direction of innovation toward an IoT-based society - Increasing dependency on uncaptured GDP in global ICT firms," Technology in Society, Elsevier, vol. 53(C), pages 23-46.
    7. Yucheng Yang & Zhong Zheng & Weinan E, 2020. "Interpretable Neural Networks for Panel Data Analysis in Economics," Papers 2010.05311, arXiv.org, revised Nov 2020.
    8. Daniel Carter & Amelia Acker & Dan Sholler, 2021. "Investigative approaches to researching information technology companies," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(6), pages 655-666, June.
    9. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
    10. Garbero, Alessandra & Sakos, Grayson & Cerulli, Giovanni, 2023. "Towards data-driven project design: Providing optimal treatment rules for development projects," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    11. Chaklader, Barnali & Gupta, Brij B. & Panigrahi, Prabin Kumar, 2023. "Analyzing the progress of FINTECH-companies and their integration with new technologies for innovation and entrepreneurship," Journal of Business Research, Elsevier, vol. 161(C).
    12. Sanghyun Kim & Bora Kim & Minsoo Seo, 2020. "Impacts of Sustainable Information Technology Capabilities on Information Security Assimilation: The Moderating Effects of Policy—Technology Balance," Sustainability, MDPI, vol. 12(15), pages 1-24, July.
    13. Maude Lavanchy & Patrick Reichert & Jayanth Narayanan & Krishna Savani, 2023. "Applicants’ Fairness Perceptions of Algorithm-Driven Hiring Procedures," Journal of Business Ethics, Springer, vol. 188(1), pages 125-150, November.
    14. Ratzanyel Rincón, 2023. "Quarterly multidimensional poverty estimates in Mexico using machine learning algorithms/Estimaciones trimestrales de pobreza multidimensional en México mediante algoritmos de aprendizaje de máquina," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 38(1), pages 3-68.
    15. Klockmann, Victor & von Schenk, Alicia & Villeval, Marie Claire, 2022. "Artificial intelligence, ethics, and intergenerational responsibility," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 284-317.
    16. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    17. Boute, Robert N. & Gijsbrechts, Joren & van Jaarsveld, Willem & Vanvuchelen, Nathalie, 2022. "Deep reinforcement learning for inventory control: A roadmap," European Journal of Operational Research, Elsevier, vol. 298(2), pages 401-412.
    18. Shan Huang & Michael Allan Ribers & Hannes Ullrich, 2021. "The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing," Discussion Papers of DIW Berlin 1939, DIW Berlin, German Institute for Economic Research.
    19. Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
    20. Daniel Bjorkegren & Joshua E. Blumenstock & Samsun Knight, 2020. "Manipulation-Proof Machine Learning," Papers 2004.03865, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:teinso:v:66:y:2021:i:c:s0160791x21001226. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/technology-in-society .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.