IDEAS home Printed from https://ideas.repec.org/a/eee/bushor/v68y2025i5p645-657.html

Fairness in crowdwork: Making the human AI supply chain more humane

Author

Listed:
  • Gonzalez-Cabello, Martin
  • Siddiq, Auyon
  • Corbett, Charles J.
  • Hu, Catherine

Abstract

The vast quantities of data required to build artificial intelligence (AI) technologies are often annotated and processed manually, making human labor a critical component of the AI supply chain. The workers who input this data are sourced through digital labor (“crowdwork”) platforms that often are unregulated and offer low wages, raising concerns about labor standards in AI development. Using the results of a survey, this article aims to shed light on the experiences and perceptions of fair treatment among workers in the AI supply chain. The study reveals significant variability in workers’ experiences, identifies potential drivers of fairness, and highlights how design choices by labor platforms can significantly affect worker welfare. Drawing on lessons from physical supply chains, this article offers practical guidance to managers on how to enhance worker welfare within the AI supply chain and how to ensure that AI technologies are responsibly sourced.

Suggested Citation

  • Gonzalez-Cabello, Martin & Siddiq, Auyon & Corbett, Charles J. & Hu, Catherine, 2025. "Fairness in crowdwork: Making the human AI supply chain more humane," Business Horizons, Elsevier, vol. 68(5), pages 645-657.
    • Handle: RePEc:eee:bushor:v:68:y:2025:i:5:p:645-657
    DOI: 10.1016/j.bushor.2024.09.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0007681324001265
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.bushor.2024.09.003?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Mohammad Amir Anwar & Mark Graham, 2020. "Digital labour at economic margins: African workers and the global information economy," Review of African Political Economy, Taylor & Francis Journals, vol. 47(163), pages 95-105, July.
    2. David Doorey, 2011. "The Transparent Supply Chain: from Resistance to Implementation at Nike and Levi-Strauss," Journal of Business Ethics, Springer, vol. 103(4), pages 587-603, November.
    3. Paolacci, Gabriele & Chandler, Jesse & Ipeirotis, Panagiotis G., 2010. "Running experiments on Amazon Mechanical Turk," Judgment and Decision Making, Cambridge University Press, vol. 5(5), pages 411-419, August.
    4. Gabriele Paolacci & Jesse Chandler & Panagiotis G. Ipeirotis, 2010. "Running experiments on Amazon Mechanical Turk," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 5(5), pages 411-419, August.
    5. repec:osf:socarx:k3hy4_v1 is not listed on IDEAS
    6. Prpić, John & Shukla, Prashant P. & Kietzmann, Jan H. & McCarthy, Ian P., 2015. "How to work a crowd: Developing crowd capital through crowdsourcing," Business Horizons, Elsevier, vol. 58(1), pages 77-85.
    7. Greg Distelhorst & Jens Hainmueller & Richard M. Locke, 2017. "Does Lean Improve Labor Standards? Management and Social Performance in the Nike Supply Chain," Management Science, INFORMS, vol. 63(3), pages 707-728, March.
    8. Schmidt, Gordon B. & Jettinghoff, William M., 2016. "Using Amazon Mechanical Turk and other compensated crowdsourcing sites," Business Horizons, Elsevier, vol. 59(4), pages 391-400.
    9. Christian Fieseler & Eliane Bucher & Christian Pieter Hoffmann, 2019. "Unfairness by Design? The Perceived Fairness of Digital Labor on Crowdworking Platforms," Journal of Business Ethics, Springer, vol. 156(4), pages 987-1005, June.
    10. Greg Distelhorst & Anita McGahan, 2022. "Socially Irresponsible Employment in Emerging-Market Manufacturers," Organization Science, INFORMS, vol. 33(6), pages 2135-2158, November.
    11. Friedland, Julian & Balkin, David B., 2023. "When gig workers become essential: Leveraging customer moral self-awareness beyond COVID-19," Business Horizons, Elsevier, vol. 66(2), pages 181-190.
    12. Jing Wang & Panagiotis G. Ipeirotis & Foster Provost, 2017. "Cost-Effective Quality Assurance in Crowd Labeling," Information Systems Research, INFORMS, vol. 28(1), pages 137-158, March.
    13. Mark Graham & Isis Hjorth & Vili Lehdonvirta, 2017. "Digital labour and development: impacts of global digital labour platforms and the gig economy on worker livelihoods," Transfer: European Review of Labour and Research, , vol. 23(2), pages 135-162, May.
    14. Merlo, Omar & Eisingerich, Andreas & Auh, Seigyoung & Levstek, Jaka, 2018. "The benefits and implementation of performance transparency: The why and how of letting your customers ‘see through’ your business," Business Horizons, Elsevier, vol. 61(1), pages 73-84.
    15. Lehdonvirta, Vili, 2018. "Flexibility in the Gig Economy: Managing Time on Three Online Piecework Platforms," SocArXiv k3hy4, Center for Open Science.
    16. Wright, Scott A. & Schultz, Ainslie E., 2018. "The rising tide of artificial intelligence and business automation: Developing an ethical framework," Business Horizons, Elsevier, vol. 61(6), pages 823-832.
    17. Andrey Shevchuk & Denis Strebkov, 2018. "Safeguards against Opportunism in Freelance Contracting on the Internet," British Journal of Industrial Relations, London School of Economics, vol. 56(2), pages 342-369, June.
    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. Cantarella, Michele & Strozzi, Chiara, 2019. "Workers in the Crowd: The Labour Market Impact of the Online Platform Economy," IZA Discussion Papers 12327, IZA Network @ LISER.
    2. Efrat Dressler & Yevgeny Mugerman, 2023. "Doing the Right Thing? The Voting Power Effect and Institutional Shareholder Voting," Journal of Business Ethics, Springer, vol. 183(4), pages 1089-1112, April.
    3. Niki Panteli & Andriana Rapti & Dora Scholarios, 2020. "‘If He Just Knew Who We Were’: Microworkers’ Emerging Bonds of Attachment in a Fragmented Employment Relationship," Work, Employment & Society, British Sociological Association, vol. 34(3), pages 476-494, June.
    4. Sestino, Andrea, 2024. "The challenge of integrating “intelligent†technologies in luxury shopping contexts: The role of brand personality appeal and consumers’ status consumption orientation," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    5. Michele Cantarella & Chiara Strozzi, 2018. "Labour market effects of crowdwork in US and EU: an empirical investigation," Department of Economics 0139, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    6. Mourelatos, Evangelos, 2021. "Personality and Ethics on Online Labor Markets: How mood influences ethical perceptions," EconStor Preprints 244735, ZBW - Leibniz Information Centre for Economics.
    7. Michele Cantarella & Chiara Strozzi, 2018. "Labour market effects of crowdwork in the US and EU: an empirical investigation," Center for Economic Research (RECent) 140, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    8. Paula McDonald & Penny Williams & Robyn Mayes & Maria Khan, 2024. "Income generation on care work digital labour platforms," British Journal of Industrial Relations, London School of Economics, vol. 62(2), pages 358-380, June.
    9. Xiaochuan Song & Graham H. Lowman & Peter Harms, 2020. "Justice for the Crowd: Organizational Justice and Turnover in Crowd-Based Labor," Administrative Sciences, MDPI, vol. 10(4), pages 1-37, November.
    10. Yamada, Katsunori & Sato, Masayuki, 2013. "Another avenue for anatomy of income comparisons: Evidence from hypothetical choice experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 89(C), pages 35-57.
    11. Sweldens, Steven & Puntoni, Stefano & Paolacci, Gabriele & Vissers, Maarten, 2014. "The bias in the bias: Comparative optimism as a function of event social undesirability," Organizational Behavior and Human Decision Processes, Elsevier, vol. 124(2), pages 229-244.
    12. S. Venus Jin & Aziz Muqaddam, 2019. "Product placement 2.0: “Do Brands Need Influencers, or Do Influencers Need Brands?”," Journal of Brand Management, Palgrave Macmillan, vol. 26(5), pages 522-537, September.
    13. Hsu, Dan K. & Burmeister-Lamp, Katrin & Simmons, Sharon A. & Foo, Maw-Der & Hong, Michelle C. & Pipes, Jesse D., 2019. "“I know I can, but I don't fit”: Perceived fit, self-efficacy, and entrepreneurial intention," Journal of Business Venturing, Elsevier, vol. 34(2), pages 311-326.
    14. Lutz, Christoph & Newlands, Gemma, 2018. "Consumer segmentation within the sharing economy: The case of Airbnb," Journal of Business Research, Elsevier, vol. 88(C), pages 187-196.
    15. Mariconda, Simone & Lurati, Francesco, 2015. "Does familiarity breed stability? The role of familiarity in moderating the effects of new information on reputation judgments," Journal of Business Research, Elsevier, vol. 68(5), pages 957-964.
    16. Gandullia, Luca & Lezzi, Emanuela, 2018. "The price elasticity of charitable giving: New experimental evidence," Economics Letters, Elsevier, vol. 173(C), pages 88-91.
    17. Tobias Schlager & Ashley V. Whillans, 2022. "People underestimate the probability of contracting the coronavirus from friends," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    18. Hou, Chenxuan & Li, Tingting & Gu, Yanzhang, 2026. "Artificial intelligence versus human providers for personalized solutions? The influence of expected group size and perceived uniqueness on adoption intentions," Journal of Retailing and Consumer Services, Elsevier, vol. 88(C).
    19. Charness, Gary & Gneezy, Uri & Kuhn, Michael A., 2013. "Experimental methods: Extra-laboratory experiments-extending the reach of experimental economics," Journal of Economic Behavior & Organization, Elsevier, vol. 91(C), pages 93-100.
    20. Gerhard, Patrick & Hoffmann, Arvid O.I. & Post, Thomas, 2017. "Past performance framing and investors’ belief updating: Is seeing long-term returns always associated with smaller belief updates?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 15(C), pages 38-51.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:eee:bushor:v:68:y:2025:i:5:p:645-657. 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: http://www.elsevier.com/locate/bushor .

    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.