IDEAS home Printed from https://ideas.repec.org/p/dar/wpaper/149346.html
   My bibliography  Save this paper

Unlocking AI’s Potential : Human Collaboration as the Catalyst

Author

Listed:
  • Buxmann, Peter
  • Ellenrieder, Sara

Abstract

Rapid advances in artificial intelligence (AI) have fueled high expectations for the technology’s potential to fundamentally transform our economy and society through automation. However, given the inscrutability and, sometimes, susceptibility to error of AI systems, we argue that the focus should shift towards fostering effective human-AI collaboration rather than pursuing automation alone. In this context, system decisions must be made available to decision-makers in an explainable and understandable manner, as further required by the EU’s recently passed AI Act. Research shows that there is potential for humans to learn from explainable AI systems and improve their own performance over time. Meanwhile, in addition to enabling humans to benefit from working with AI systems on various everyday tasks, such collaboration ensures the safe and reliable use of AI systems, especially in high-risk areas such as medicine, where human oversight remains paramount.

Suggested Citation

  • Buxmann, Peter & Ellenrieder, Sara, 2024. "Unlocking AI’s Potential : Human Collaboration as the Catalyst," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 149346, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    • Handle: RePEc:dar:wpaper:149346
    Note: for complete metadata visit http://tubiblio.ulb.tu-darmstadt.de/149346/
    as

    Download full text from publisher

    File URL: https://ojs.weizenbaum-institut.de/index.php/wjds/article/view/4_1_7
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Timm Teubner & Christoph M. Flath & Christof Weinhardt & Wil Aalst & Oliver Hinz, 2023. "Welcome to the Era of ChatGPT et al," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(2), pages 95-101, April.
    2. Pumplun, Luisa & Peters, Felix & Gawlitza, Joshua & Buxmann, Peter, 2023. "Bringing Machine Learning Systems into Clinical Practice: A Design Science Approach to Explainable Machine Learning-Based Clinical Decision Support Systems," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 138523, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    3. Dominik Siemon, 2022. "Elaborating Team Roles for Artificial Intelligence-based Teammates in Human-AI Collaboration," Group Decision and Negotiation, Springer, vol. 31(5), pages 871-912, October.
    4. Sturm, Timo & Gerlach, Jin & Pumplun, Luisa & Mesbah, Neda & Peters, Felix & Tauchert, Christoph & Nan, Ning & Buxmann, Peter, 2021. "Coordinating Human and Machine Learning for Effective Organizational Learning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 125653, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. Davide Castelvecchi, 2016. "Can we open the black box of AI?," Nature, Nature, vol. 538(7623), pages 20-23, October.
    Full references (including those not matched with items on IDEAS)

    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. Alireza Rezazadeh & Yasamin Jafarian & Ali Kord, 2022. "Explainable Ensemble Machine Learning for Breast Cancer Diagnosis Based on Ultrasound Image Texture Features," Forecasting, MDPI, vol. 4(1), pages 1-13, February.
    2. Hsu, Chia-Wei & Xiong, Rui & Chen, Nan-Yow & Li, Ju & Tsou, Nien-Ti, 2022. "Deep neural network battery life and voltage prediction by using data of one cycle only," Applied Energy, Elsevier, vol. 306(PB).
    3. Laith T. Khrais, 2020. "Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce," Future Internet, MDPI, vol. 12(12), pages 1-14, December.
    4. Henner Gimpel & Vanessa Graf-Seyfried & Robert Laubacher & Oliver Meindl, 2023. "Towards Artificial Intelligence Augmenting Facilitation: AI Affordances in Macro-Task Crowdsourcing," Group Decision and Negotiation, Springer, vol. 32(1), pages 75-124, February.
    5. Ekaterina Jussupow & Kai Spohrer & Armin Heinzl, 2022. "Radiologists’ Usage of Diagnostic AI Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(3), pages 293-309, June.
    6. Martin Eling & Davide Nuessle & Julian Staubli, 2022. "The impact of artificial intelligence along the insurance value chain and on the insurability of risks," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(2), pages 205-241, April.
    7. Canellas, Marc & Haga, Rachel, 2017. "Framing Human-Automation Regulation: A New Modus Operandi from Cognitive Engineering," LawArXiv yu2h3, Center for Open Science.
    8. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method," Applied Energy, Elsevier, vol. 348(C).
    9. Alwosheel, Ahmad & van Cranenburgh, Sander & Chorus, Caspar G., 2018. "Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis," Journal of choice modelling, Elsevier, vol. 28(C), pages 167-182.
    10. Eduardo Graells-Garrido & Vanessa Peña-Araya & Loreto Bravo, 2020. "Adoption-Driven Data Science for Transportation Planning: Methodology, Case Study, and Lessons Learned," Sustainability, MDPI, vol. 12(15), pages 1-17, July.
    11. Pujin Wang & Jianzhuang Xiao & Ken’ichi Kawaguchi & Lichen Wang, 2022. "Automatic Ceiling Damage Detection in Large-Span Structures Based on Computer Vision and Deep Learning," Sustainability, MDPI, vol. 14(6), pages 1-24, March.
    12. Chenfeng Yan & Quan Chen & Xinyue Zhou & Xin Dai & Zhilin Yang, 2024. "When the Automated fire Backfires: The Adoption of Algorithm-based HR Decision-making Could Induce Consumer’s Unfavorable Ethicality Inferences of the Company," Journal of Business Ethics, Springer, vol. 190(4), pages 841-859, April.
    13. Brian G Booth & Eva Hoefnagels & Toon Huysmans & Jan Sijbers & Noël L W Keijsers, 2020. "PAPPI: Personalized analysis of plantar pressure images using statistical modelling and parametric mapping," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-22, February.
    14. Dominic Chalmers & Niall G. MacKenzie & Sara Carter, 2021. "Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution," Entrepreneurship Theory and Practice, , vol. 45(5), pages 1028-1053, September.
    15. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
    16. Pei-Chen Tsai & Tsung-Hua Lee & Kun-Chi Kuo & Fang-Yi Su & Tsung-Lu Michael Lee & Eliana Marostica & Tomotaka Ugai & Melissa Zhao & Mai Chan Lau & Juha P. Väyrynen & Marios Giannakis & Yasutoshi Takas, 2023. "Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    17. Janik Ole Wecks & Johannes Voshaar & Benedikt Jost Plate & Jochen Zimmermann, 2024. "Generative AI Usage and Exam Performance," Papers 2404.19699, arXiv.org, revised Nov 2024.
    18. Hui Niu & Siyuan Li & Jian Li, 2022. "MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization," Papers 2210.01774, arXiv.org.
    19. Cristina Lee, 2024. "Artificial Neural Networks (ANNs) and Machine Learning (ML) Modeling Employee Behavior with Management Towards the Economic Advancement of Workers," Sustainability, MDPI, vol. 16(21), pages 1-17, November.
    20. Michael Meiser & Ingo Zinnikus, 2024. "A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities," Energies, MDPI, vol. 17(9), pages 1-29, April.

    More about this item

    Statistics

    Access and download statistics

    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:dar:wpaper:149346. 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: Dekanatssekretariat (email available below). General contact details of provider: https://edirc.repec.org/data/ivthdde.html .

    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.