IDEAS home Printed from https://ideas.repec.org/p/zbw/hbsfof/155.html
   My bibliography  Save this paper

Crowdsourced production of AI Training Data: How human workers teach self-driving cars how to see

Author

Listed:
  • Schmidt, Florian Alexander

Abstract

Since 2017 the automotive industry has developed a high demand for ground truth data. Without this data, the ambitious goal of producing fully autonomous vehicles will remain out of reach. The self-driving car depends on self-learning algorithms, which in turn have to undergo a lot of supervised training. This requires vast amounts of manual labour, performed by crowdworkers across the globe. As a consequence, the demand in training data is transforming the crowdsourcing industry. This study is an investigation into the dynamics of this shift and its impacts on the working conditions of the crowdworkers.

Suggested Citation

  • Schmidt, Florian Alexander, 2019. "Crowdsourced production of AI Training Data: How human workers teach self-driving cars how to see," Working Paper Forschungsförderung 155, Hans-Böckler-Stiftung, Düsseldorf.
    • Handle: RePEc:zbw:hbsfof:155
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/216075/1/hbs-fofoe-wp-155-2019.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pamucar, Dragan & Deveci, Muhammet & Gokasar, Ilgin & Tavana, Madjid & Köppen, Mario, 2022. "A metaverse assessment model for sustainable transportation using ordinal priority approach and Aczel-Alsina norms," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    2. Janine Berg & Miriam A. Cherry & Uma Rani, 2019. "Digital labour platforms: a need for international regulation?," Revista de Economía Laboral - Spanish Journal of Labour Economics, Asociación Española de Economía Laboral - AEET, vol. 16, pages 104-128.

    More about this item

    Keywords

    crowdworking; artificial Intelligence; self-driving cars; automotive industry; global labour markets; AI;
    All these keywords.

    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:zbw:hbsfof:155. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/boeckde.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.