IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v242y2024ics0165176524003525.html
   My bibliography  Save this article

Will user-contributed AI training data eat its own tail?

Author

Listed:
  • Gans, Joshua S.

Abstract

This paper examines and finds that the answer is likely to be no. The environment examined starts with users who contribute based on their motives to create a public good. Their own actions determine the quality of that public good but also embed a free-rider problem. When AI is trained on that data, it can generate similar contributions to the public good. It is shown that this increases the incentive of human users to provide contributions that are more costly to supply. Thus, the overall quality of contributions from both AI and humans rises compared to human-only contributions. In situations where platform providers want to generate more contributions using explicit incentives, the rate of return on such incentives is shown to be lower in this environment.

Suggested Citation

  • Gans, Joshua S., 2024. "Will user-contributed AI training data eat its own tail?," Economics Letters, Elsevier, vol. 242(C).
  • Handle: RePEc:eee:ecolet:v:242:y:2024:i:c:s0165176524003525
    DOI: 10.1016/j.econlet.2024.111868
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176524003525
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econlet.2024.111868?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Lena Abou El-Komboz & Moritz Goldbeck, 2023. "Career Concerns As Public Good: The Role of Signaling for Open Source Software Development," Rationality and Competition Discussion Paper Series 453, CRC TRR 190 Rationality and Competition.
    2. Josh Lerner & Jean Tirole, 2002. "Some Simple Economics of Open Source," Journal of Industrial Economics, Wiley Blackwell, vol. 50(2), pages 197-234, June.
    3. Barbosu, Sandra & Gans, Joshua S., 2022. "Storm crowds: Evidence from Zooniverse on crowd contribution design," Research Policy, Elsevier, vol. 51(1).
    4. Lei Xu & Tingting Nian & Luis Cabral, 2018. "What Makes Geeks Tick? A Study of Stack Overflow Careers," Working Papers 18-04, New York University, Leonard N. Stern School of Business, Department of Economics.
    5. Lei Xu & Tingting Nian & Luís Cabral, 2020. "What Makes Geeks Tick? A Study of Stack Overflow Careers," Management Science, INFORMS, vol. 66(2), pages 587-604, February.
    6. Engers, Maxim & Gans, Joshua S, 1998. "Why Referees Are Not Paid (Enough)," American Economic Review, American Economic Association, vol. 88(5), pages 1341-1349, December.
    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. Jing Wang & Gen Li & Kai-Lung Hui, 2022. "Monetary Incentives and Knowledge Spillover: Evidence from a Natural Experiment," Management Science, INFORMS, vol. 68(5), pages 3549-3572, May.
    2. Charles Ayoubi & Boris Thurm, 2023. "Knowledge diffusion and morality: Why do we freely share valuable information with Strangers?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 32(1), pages 75-99, January.
    3. Erdem Dogukan Yilmaz & Tim Meyer & Milan Miric, 2023. "Preventing Others from Commercializing Your Innovation: Evidence from Creative Commons Licenses," Papers 2309.00536, arXiv.org.
    4. Yi Yang & Kunpeng Zhang & Yangyang Fan, 2023. "sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics," Information Systems Research, INFORMS, vol. 34(1), pages 137-156, March.
    5. Chowdhury Mohammad Sakib Anwar & Konstantinos Georgalos, 2024. "Position uncertainty in a sequential public goods game: an experiment," Experimental Economics, Springer;Economic Science Association, vol. 27(4), pages 820-853, September.
    6. Jiang, Zhi-Qiang & Wang, Peng & Ma, Jun-Chao & Zhu, Peican & Han, Zhen & Podobnik, Boris & Stanley, H. Eugene & Zhou, Wei-Xing & Alfaro-Bittner, Karin & Boccaletti, Stefano, 2023. "Unraveling the effects of network, direct and indirect reciprocity in online societies," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    7. Miric, Milan & Jeppesen, Lars Bo, 2023. "How does competition influence innovative effort within a platform-based ecosystem? Contrasting paid and unpaid contributors," Research Policy, Elsevier, vol. 52(7).
    8. Jacqmin, Julien, 2018. "Why are some online courses more open than others?," MPRA Paper 89929, University Library of Munich, Germany.
    9. Engelhardt, Sebastian v. & Freytag, Andreas, 2013. "Institutions, culture, and open source," Journal of Economic Behavior & Organization, Elsevier, vol. 95(C), pages 90-110.
    10. Josh Lerner, 2005. "The Scope of Open Source Licensing," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 21(1), pages 20-56, April.
    11. Batiz-Lazo, Bernardo & Krichel, Thomas, 2010. "The creation of internet communities: A brief history of on-line distribution of working papers through NEP, 1998-2010," MPRA Paper 27085, University Library of Munich, Germany.
    12. Graziella Marzi, 2009. "If not for money for what? Digging into the OS/FS contributors’ motivations," Working Papers 166, University of Milano-Bicocca, Department of Economics, revised Jul 2009.
    13. Bitzer, Jürgen & Geishecker, Ingo, 2010. "Who contributes voluntarily to OSS? An investigation among German IT employees," Research Policy, Elsevier, vol. 39(1), pages 165-172, February.
    14. Arthur Schram & Boris Van Leeuwen & Theo Offerman, 2013. "Superstars Need Social Benefits: An Experiment on Network Formation," Working Papers 1306, Departament Empresa, Universitat Autònoma de Barcelona, revised Jul 2013.
    15. Fabio M. Manenti & Stefano Comino & Marialaura Parisi, 2005. "From Planning to Mature: on the Determinants of Open Source Take-Off," Industrial Organization 0507006, University Library of Munich, Germany, revised 29 Sep 2005.
    16. Kevin J. Boudreau & Andrei Hagiu, 2009. "Platform Rules: Multi-Sided Platforms as Regulators," Chapters, in: Annabelle Gawer (ed.), Platforms, Markets and Innovation, chapter 7, Edward Elgar Publishing.
    17. David, Paul A. & Shapiro, Joseph S., 2008. "Community-based production of open-source software: What do we know about the developers who participate?," Information Economics and Policy, Elsevier, vol. 20(4), pages 364-398, December.
    18. Liuan Wang & Lu (Lucy) Yan & Tongxin Zhou & Xitong Guo & Gregory R. Heim, 2020. "Understanding Physicians’ Online-Offline Behavior Dynamics: An Empirical Study," Information Systems Research, INFORMS, vol. 31(2), pages 537-555, June.
    19. Luigi Di Gaetano, 2015. "A Model of corporate donations to open source under hardware–software complementarity," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 24(1), pages 163-190.
    20. Stam, Wouter, 2009. "When does community participation enhance the performance of open source software companies?," Research Policy, Elsevier, vol. 38(8), pages 1288-1299, October.

    More about this item

    Keywords

    Artificial intelligence; Training data; User contributions; Prediction;
    All these keywords.

    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • D70 - Microeconomics - - Analysis of Collective Decision-Making - - - General
    • H44 - Public Economics - - Publicly Provided Goods - - - Publicly Provided Goods: Mixed Markets

    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:ecolet:v:242:y:2024:i:c:s0165176524003525. 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/ecolet .

    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.