IDEAS home Printed from https://ideas.repec.org/p/zbw/zewpbs/022023.html
   My bibliography  Save this paper

Human oversight done right: The AI Act should use humans to monitor AI only when effective

Author

Listed:
  • Walter, Johannes

Abstract

The EU's proposed Artificial Intelligence Act (AI Act) is meant to ensure safe AI systems in high-risk applications. The Act relies on human supervision of machine-learning algorithms, yet mounting evidence indicates that such oversight is not always reliable. In many cases, humans cannot accurately assess the quality of algorithmic recommendations, and thus fail to prevent harmful behaviour. This policy brief proposes three ways to solve the problem: First, Article 14 of the AI Act should be revised to acknowledge that humans often have difficulty assessing recommendations made by algorithms. Second, the suitability of human oversight for preventing harmful outcomes should be empirically tested for every high-risk application under consideration. Third, following Biermann et al. (2022), human decision-makers should receive feedback on past decisions to enable learning and improve future decisions.

Suggested Citation

  • Walter, Johannes, 2023. "Human oversight done right: The AI Act should use humans to monitor AI only when effective," ZEW policy briefs 02/2023, ZEW - Leibniz Centre for European Economic Research.
    • Handle: RePEc:zbw:zewpbs:022023
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/271285/1/1838979220.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Biermann, Jan & Horton, John J. & Walter, Johannes, 2022. "Algorithmic advice as a credence good," ZEW Discussion Papers 22-071, ZEW - Leibniz Centre for European Economic Research.
    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.

      More about this item

      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:zewpbs:022023. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/zemande.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.