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Unlocking AI’s Potential : Human Collaboration as the Catalyst

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  • 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/
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    References listed on IDEAS

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    1. 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).
    2. 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).
    3. 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.
    4. 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.
    5. Davide Castelvecchi, 2016. "Can we open the black box of AI?," Nature, Nature, vol. 538(7623), pages 20-23, October.
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