IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v33y2022i2p678-696.html
   My bibliography  Save this article

Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation

Author

Listed:
  • Andreas Fügener

    (University of Cologne, 50923 Cologne, Germany)

  • Jörn Grahl

    (University of Cologne, 50923 Cologne, Germany)

  • Alok Gupta

    (University of Minnesota, Minneapolis, Minnesota 55455)

  • Wolfgang Ketter

    (University of Cologne, 50923 Cologne, Germany; Erasmus University Rotterdam, 3062 PA Rotterdam, Netherlands)

Abstract

We study how humans make decisions when they collaborate with an artificial intelligence (AI) in a setting where humans and the AI perform classification tasks. Our experimental results suggest that humans and AI who work together can outperform the AI that outperforms humans when it works on its own. However, the combined performance improves only when the AI delegates work to humans but not when humans delegate work to the AI. The AI’s delegation performance improved even when it delegated to low-performing subjects; by contrast, humans did not delegate well and did not benefit from delegation to the AI. This bad delegation performance cannot be explained with some kind of algorithm aversion. On the contrary, subjects acted rationally in an internally consistent manner by trying to follow a proven delegation strategy and appeared to appreciate the AI support. However, human performance suffered as a result of a lack of metaknowledge—that is, humans were not able to assess their own capabilities correctly, which in turn led to poor delegation decisions. Lacking metaknowledge, in contrast to reluctance to use AI, is an unconscious trait. It fundamentally limits how well human decision makers can collaborate with AI and other algorithms. The results have implications for the future of work, the design of human–AI collaborative environments, and education in the digital age.

Suggested Citation

  • Andreas Fügener & Jörn Grahl & Alok Gupta & Wolfgang Ketter, 2022. "Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation," Information Systems Research, INFORMS, vol. 33(2), pages 678-696, June.
    • Handle: RePEc:inm:orisre:v:33:y:2022:i:2:p:678-696
    DOI: 10.1287/isre.2021.1079
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/isre.2021.1079
    Download Restriction: no
    File URL: https://libkey.io/10.1287/isre.2021.1079?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
    ---><---

    References listed on IDEAS

    as
    1. John Horton & David Rand & Richard Zeckhauser, 2011. "The online laboratory: conducting experiments in a real labor market," Experimental Economics, Springer;Economic Science Association, vol. 14(3), pages 399-425, September.
    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    3. Jussupow, Ekaterina & Spohrer, Kai & Heinzl, Armin & Gawlitza, Joshua, 2021. "Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 137446, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue Nov.
    5. Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
    6. Ruyi Ge & Zhiqiang (Eric) Zheng & Xuan Tian & Li Liao, 2021. "Human–Robot Interaction: When Investors Adjust the Usage of Robo-Advisors in Peer-to-Peer Lending," Information Systems Research, INFORMS, vol. 32(3), pages 774-785, September.
    7. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    8. David Autor, 2014. "Polanyi's Paradox and the Shape of Employment Growth," NBER Working Papers 20485, National Bureau of Economic Research, Inc.
    9. Mirko Kremer & Brent Moritz & Enno Siemsen, 2011. "Demand Forecasting Behavior: System Neglect and Change Detection," Management Science, INFORMS, vol. 57(10), pages 1827-1843, October.
    10. Logg, Jennifer M. & Minson, Julia A. & Moore, Don A., 2019. "Algorithm appreciation: People prefer algorithmic to human judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 90-103.
    11. Coppock, Alexander, 2019. "Generalizing from Survey Experiments Conducted on Mechanical Turk: A Replication Approach," Political Science Research and Methods, Cambridge University Press, vol. 7(3), pages 613-628, July.
    12. Yun Shin Lee & Yong Won Seo & Enno Siemsen, 2018. "Running Behavioral Operations Experiments Using Amazon's Mechanical Turk," Production and Operations Management, Production and Operations Management Society, vol. 27(5), pages 973-989, May.
    13. Nosek, Brian A. & Ebersole, Charles R. & DeHaven, Alexander Carl & Mellor, David Thomas, 2018. "The Preregistration Revolution," OSF Preprints 2dxu5, Center for Open Science.
    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. Araz Zirar, 2023. "Can artificial intelligence’s limitations drive innovative work behaviour?," Review of Managerial Science, Springer, vol. 17(6), pages 2005-2034, August.
    2. Binh Nguyen Thanh & Ha Xuan Son & Diem Thi Hong Vo, 2024. "Blockchain: The Economic and Financial Institution for Autonomous AI?," JRFM, MDPI, vol. 17(2), pages 1-20, January.
    3. Martin Adam & Konstantin Roethke & Alexander Benlian, 2023. "Human vs. Automated Sales Agents: How and Why Customer Responses Shift Across Sales Stages," Information Systems Research, INFORMS, vol. 34(3), pages 1148-1168, September.
    4. Zirar, Araz & Ali, Syed Imran & Islam, Nazrul, 2023. "Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda," Technovation, Elsevier, vol. 124(C).
    5. Ivanov, Dmitry, 2023. "Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability," International Journal of Production Economics, Elsevier, vol. 263(C).
    6. Wang, Weisha & Wang, Yichuan & Chen, Long & Ma, Rui & Zhang, Minhao, 2024. "Justice at the Forefront: Cultivating felt accountability towards Artificial Intelligence among healthcare professionals," Social Science & Medicine, Elsevier, vol. 347(C).
    7. Jayarajan Samuel & Zhiqiang (Eric) Zheng & Vijay Mookerjee, 2024. "Task Characteristics and Incentives in Collaborative Problem Solving: Evidence from Three Field Experiments," Information Systems Research, INFORMS, vol. 35(1), pages 414-433, March.
    8. Mario Passalacqua & Robert Pellerin & Florian Magnani & Philippe Doyon-Poulin & Laurène Del-Aguila & Jared Boasen & Pierre-Majorique Léger, 2024. "Human-centred AI in industry 5.0: a systematic review," Post-Print hal-04723054, HAL.

    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. Kevin Bauer & Andrej Gill, 2024. "Mirror, Mirror on the Wall: Algorithmic Assessments, Transparency, and Self-Fulfilling Prophecies," Information Systems Research, INFORMS, vol. 35(1), pages 226-248, March.
    2. Ivanov, Dmitry, 2023. "Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability," International Journal of Production Economics, Elsevier, vol. 263(C).
    3. Kevin Bauer & Moritz von Zahn & Oliver Hinz, 2023. "Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing," Information Systems Research, INFORMS, vol. 34(4), pages 1582-1602, December.
    4. Abel Brodeur, Nikolai M. Cook, Anthony Heyes, 2022. "We Need to Talk about Mechanical Turk: What 22,989 Hypothesis Tests Tell Us about Publication Bias and p-Hacking in Online Experiments," LCERPA Working Papers am0133, Laurier Centre for Economic Research and Policy Analysis.
    5. Brodeur, Abel & Cook, Nikolai & Heyes, Anthony, 2022. "We Need to Talk about Mechanical Turk: What 22,989 Hypothesis Tests Tell us about p-Hacking and Publication Bias in Online Experiments," GLO Discussion Paper Series 1157, Global Labor Organization (GLO).
    6. Keding, Christoph & Meissner, Philip, 2021. "Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    7. Daniel Susskind, 2017. "Re-Thinking the Capabilities of Machines in Economics," Economics Series Working Papers 825, University of Oxford, Department of Economics.
    8. Johannes G. Jaspersen & Marc A. Ragin & Justin R. Sydnor, 2022. "Insurance demand experiments: Comparing crowdworking to the lab," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(4), pages 1077-1107, December.
    9. Samuel N. Kirshner & Brent B. Moritz, 2023. "For the future and from afar: Psychological distance and inventory decision‐making," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 170-188, January.
    10. Daniel Susskind, 2019. "Re-thinking the capabilities of technology in economics," Economics Bulletin, AccessEcon, vol. 39(1), pages 280-288.
    11. Christiane B. Haubitz & Cedric A. Lehmann & Andreas Fügener & Ulrich W. Thonemann, 2021. "The Risk of Algorithm Transparency: How Algorithm Complexity Drives the Effects on Use of Advice," ECONtribute Discussion Papers Series 078, University of Bonn and University of Cologne, Germany.
    12. von Walter, Benjamin & Wentzel, Daniel & Raff, Stefan, 2023. "Should service firms introduce algorithmic advice to their existing customers? The moderating effect of service relationships," Journal of Retailing, Elsevier, vol. 99(2), pages 280-296.
    13. Said Kaawach & Oskar Kowalewski & Oleksandr Talavera, 2023. "Automatic vs Manual Investing: Role of Past Performance," Discussion Papers 23-04, Department of Economics, University of Birmingham.
    14. Xueming Luo & Siliang Tong & Zheng Fang & Zhe Qu, 2019. "Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases," Marketing Science, INFORMS, vol. 38(6), pages 937-947, November.
    15. Fumagalli, Elena & Rezaei, Sarah & Salomons, Anna, 2022. "OK computer: Worker perceptions of algorithmic recruitment," Research Policy, Elsevier, vol. 51(2).
    16. Huang, Xiaozhi & Wu, Xitong & Cao, Xin & Wu, Jifei, 2023. "The effect of medical artificial intelligence innovation locus on consumer adoption of new products," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    17. Aleksandra Parteka & Joanna Wolszczak-Derlacz, 2020. "Wage response to global production links: evidence for workers from 28 European countries (2005–2014)," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 156(4), pages 769-801, November.
    18. David Deming & Lisa B. Kahn, 2018. "Skill Requirements across Firms and Labor Markets: Evidence from Job Postings for Professionals," Journal of Labor Economics, University of Chicago Press, vol. 36(S1), pages 337-369.
    19. Christoph Riedl & Eric Bogert, 2024. "Effects of AI Feedback on Learning, the Skill Gap, and Intellectual Diversity," Papers 2409.18660, arXiv.org.
    20. Lin Lu & Laurent Dercle & Binsheng Zhao & Lawrence H. Schwartz, 2021. "Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging," Nature Communications, Nature, vol. 12(1), pages 1-11, December.

    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:inm:orisre:v:33:y:2022:i:2:p:678-696. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.