IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp14545.html
   My bibliography  Save this paper

Crowdsourcing Artificial Intelligence in Africa: Findings from a Machine Learning Contest

Author

Listed:
  • Naudé, Wim

    (RWTH Aachen University)

  • Bray, Amy

    (Zindi)

  • Lee, Celina

    (Zindi)

Abstract

In this paper, we study the crowdsourcing of innovation in Africa through a data science contest on an intermediated digital platform. We ran a Machine Learning (ML) contest on the continent's largest data science contest platform, Zindi. Contestants were surveyed on their motivations to take part and their perceptions about AI in Africa. In total, 614 contestants submitted 15,832 entries, and 559 responded to the accompanying survey. From the findings, we answered several questions: who take part in these contests and why? Who is most likely to win? What are contestants' entrepreneurial aspirations in deploying AI? What are the obstacles they perceive to the greater diffusion of AI in Africa? We conclude that crowdsourcing of AI via data contest platforms offers a potential mechanism to alleviate some of the constraints in the adoption and diffusion of AI in Africa. Recommendations for further research are made.

Suggested Citation

  • Naudé, Wim & Bray, Amy & Lee, Celina, 2021. "Crowdsourcing Artificial Intelligence in Africa: Findings from a Machine Learning Contest," IZA Discussion Papers 14545, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp14545
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp14545.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Benny Moldovanu & Aner Sela, 2008. "The Optimal Allocation of Prizes in Contests," Springer Books, in: Roger D. Congleton & Arye L. Hillman & Kai A. Konrad (ed.), 40 Years of Research on Rent Seeking 1, pages 615-631, Springer.
    2. Emmanuel W. Ayaburi & Jaeung Lee & Michele Maasberg, 0. "Understanding Crowdsourcing Contest Fitness Strategic Decision Factors and Performance: An Expectation-Confirmation Theory Perspective," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
    3. Ben Taieb, Souhaib & Hyndman, Rob J., 2014. "A gradient boosting approach to the Kaggle load forecasting competition," International Journal of Forecasting, Elsevier, vol. 30(2), pages 382-394.
    4. Moldovanu, Benny & Sela, Aner, 2006. "Contest architecture," Journal of Economic Theory, Elsevier, vol. 126(1), pages 70-96, January.
    5. Kumar, Sanjesh & Singh, Baljeet, 2019. "Barriers to the international diffusion of technological innovations," Economic Modelling, Elsevier, vol. 82(C), pages 74-86.
    6. Devon Brackbill & Damon Centola, 2020. "Impact of network structure on collective learning: An experimental study in a data science competition," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-13, September.
    7. Wolfgang Keller, 2004. "International Technology Diffusion," Journal of Economic Literature, American Economic Association, vol. 42(3), pages 752-782, September.
    8. repec:cte:werepe:we075126 is not listed on IDEAS
    9. Farzin, Y. H. & Huisman, K. J. M. & Kort, P. M., 1998. "Optimal timing of technology adoption," Journal of Economic Dynamics and Control, Elsevier, vol. 22(5), pages 779-799, May.
    10. Abramovitz, Moses, 1986. "Catching Up, Forging Ahead, and Falling Behind," The Journal of Economic History, Cambridge University Press, vol. 46(2), pages 385-406, June.
    11. Yuxiang Zhao & Qinghua Zhu, 2014. "Evaluation on crowdsourcing research: Current status and future direction," Information Systems Frontiers, Springer, vol. 16(3), pages 417-434, July.
    12. Daniel P. Gross, 2020. "Creativity Under Fire: The Effects of Competition on Creative Production," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 583-599, July.
    13. Bojer, Casper Solheim & Meldgaard, Jens Peder, 2021. "Kaggle forecasting competitions: An overlooked learning opportunity," International Journal of Forecasting, Elsevier, vol. 37(2), pages 587-603.
    14. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 23-57, National Bureau of Economic Research, Inc.
    15. Trajtenberg, Manuel, 2018. "AI as the next GPT: a Political-Economy Perspective," CEPR Discussion Papers 12721, C.E.P.R. Discussion Papers.
    16. Nikolas Zolas & Zachary Kroff & Erik Brynjolfsson & Kristina McElheran & David Beede & Catherine Buffington & Nathan Goldschlag & Lucia Foster & Emin Dinlersoz, 2020. "Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey," Working Papers 20-40, Center for Economic Studies, U.S. Census Bureau.
    17. Luis Corchón, 2007. "The theory of contests: a survey," Review of Economic Design, Springer;Society for Economic Design, vol. 11(2), pages 69-100, September.
    18. Ersin Körpeoğlu & Soo-Haeng Cho, 2018. "Incentives in Contests with Heterogeneous Solvers," Management Science, INFORMS, vol. 64(6), pages 2709-2715, June.
    19. Moustapha Cisse, 2018. "Look to Africa to advance artificial intelligence," Nature, Nature, vol. 562(7728), pages 461-461, October.
    20. 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.
    21. Sanna Ojanperä & Mark Graham & Matthew Zook, 2019. "The Digital Knowledge Economy Index: Mapping Content Production," Journal of Development Studies, Taylor & Francis Journals, vol. 55(12), pages 2626-2643, December.
    22. Emmanuel W. Ayaburi & Jaeung Lee & Michele Maasberg, 2020. "Understanding Crowdsourcing Contest Fitness Strategic Decision Factors and Performance: An Expectation-Confirmation Theory Perspective," Information Systems Frontiers, Springer, vol. 22(5), pages 1227-1240, October.
    23. Anna Gatzioura & Miquel Sànchez-Marrè & Karina Gibert, 2019. "A Hybrid Recommender System to Improve Circular Economy in Industrial Symbiotic Networks," Energies, MDPI, vol. 12(18), pages 1-24, September.
    24. Boyan Jovanovic & Rafael Rob, 1989. "The Growth and Diffusion of Knowledge," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 56(4), pages 569-582.
    25. Acar, Oguz A., 2019. "Motivations and solution appropriateness in crowdsourcing challenges for innovation," Research Policy, Elsevier, vol. 48(8), pages 1-1.
    26. Segev, Ella, 2020. "Crowdsourcing contests," European Journal of Operational Research, Elsevier, vol. 281(2), pages 241-255.
    27. Ricardo Vinuesa & Hossein Azizpour & Iolanda Leite & Madeline Balaam & Virginia Dignum & Sami Domisch & Anna Felländer & Simone Daniela Langhans & Max Tegmark & Francesco Fuso Nerini, 2020. "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    28. repec:eee:respol:v:48:y:2019:i:8:p:- is not listed on IDEAS
    29. Tauchert, Christoph & Buxmann, Peter & Lambinus, Jannis, 2020. "Crowdsourcing Data Science: A Qualitative Analysis of Organizations’ Usage of Kaggle Competitions," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 117656, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    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. Segev, Ella, 2020. "Crowdsourcing contests," European Journal of Operational Research, Elsevier, vol. 281(2), pages 241-255.
    2. Hou, Ting & Zhang, Wen, 2021. "Optimal two-stage elimination contests for crowdsourcing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    3. Livio Cricelli & Michele Grimaldi & Silvia Vermicelli, 2022. "Crowdsourcing and open innovation: a systematic literature review, an integrated framework and a research agenda," Review of Managerial Science, Springer, vol. 16(5), pages 1269-1310, July.
    4. Subhasish M. Chowdhury & Patricia Esteve‐González & Anwesha Mukherjee, 2023. "Heterogeneity, leveling the playing field, and affirmative action in contests," Southern Economic Journal, John Wiley & Sons, vol. 89(3), pages 924-974, January.
    5. Krumer, Alex & Megidish, Reut & Sela, Aner, 2023. "Strategic manipulations in round-robin tournaments," Mathematical Social Sciences, Elsevier, vol. 122(C), pages 50-57.
    6. Tekic, Anja & Alfonzo Pacheco, Diana Vilma, 2024. "Contest design and solvers' engagement behaviour in crowdsourcing: The neo-configurational perspective," Technovation, Elsevier, vol. 132(C).
    7. Bo Chen & Emilios Galariotis & Lijun Ma & Zijia Wang & Zhaobo Zhu, 2023. "On disclosure of participation in innovation contests: a dominance result," Annals of Operations Research, Springer, vol. 328(2), pages 1615-1629, September.
    8. Joel O. Wooten, 2022. "Leaps in innovation and the Bannister effect in contests," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2646-2663, June.
    9. Hinnosaar, Toomas, 2024. "Optimal sequential contests," Theoretical Economics, Econometric Society, vol. 19(1), January.
    10. Meschi, Elena & Taymaz, Erol & Vivarelli, Marco, 2011. "Trade, technology and skills: Evidence from Turkish microdata," Labour Economics, Elsevier, vol. 18(S1), pages 60-70.
    11. Ghazala Azmat & Marc Möller, 2018. "The Distribution of Talent Across Contests," Economic Journal, Royal Economic Society, vol. 128(609), pages 471-509, March.
    12. Venturini, Francesco, 2022. "Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 220-243.
    13. Ta, Chi L., 2024. "Do conservation contests work? An analysis of a large-scale energy competitive rebate program," Journal of Environmental Economics and Management, Elsevier, vol. 124(C).
    14. Jan Fagerberg & Maryann Feldman & Martin Srholec, 2011. "Technological Dynamics and Social Capability: Comparing U.S. States and European Nations," Working Papers on Innovation Studies 20111114, Centre for Technology, Innovation and Culture, University of Oslo.
    15. Klaus Desmet & Felipe Meza & Juan A. Rojas, 2008. "Foreign direct investment and spillovers: gradualism may be better," Canadian Journal of Economics, Canadian Economics Association, vol. 41(3), pages 926-953, August.
    16. Alexander Matros, 2006. "Elimination Tournaments where Players Have Fixed Resources," Working Paper 205, Department of Economics, University of Pittsburgh, revised Jan 2006.
    17. Minchuk, Yizhaq & Sela, Aner, 2023. "Subsidy and taxation in all-pay auctions under incomplete information," Games and Economic Behavior, Elsevier, vol. 140(C), pages 99-114.
    18. Castellacci, Fulvio & Natera, Jose Miguel, 2013. "The dynamics of national innovation systems: A panel cointegration analysis of the coevolution between innovative capability and absorptive capacity," Research Policy, Elsevier, vol. 42(3), pages 579-594.
    19. Arnab Bhattacharjee & Eduardo Castro & Chris Jensen-Butler, 2009. "Regional variation in productivity: a study of the Danish economy," Journal of Productivity Analysis, Springer, vol. 31(3), pages 195-212, June.
    20. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.

    More about this item

    Keywords

    crowdsourcing; innovation; data science; artificial intelligence; Africa;
    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
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O36 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Open Innovation
    • O55 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Africa

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:iza:izadps:dp14545. 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: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.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.