IDEAS home Printed from https://ideas.repec.org/a/taf/tjisxx/v31y2022i3p313-338.html
   My bibliography  Save this article

Algorithmic decision-making and system destructiveness: A case of automatic debt recovery

Author

Listed:
  • Tapani Rinta-Kahila
  • Ida Someh
  • Nicole Gillespie
  • Marta Indulska
  • Shirley Gregor

Abstract

Governments are increasingly relying on algorithmic decision-making (ADM) to deliver public services. Recent information systems literature has raised concerns regarding ADM’s negative unintended consequences, such as widespread discrimination, which in extreme cases can be destructive to society. The extant empirical literature, however, has not sufficiently examined the destructive effects of governmental ADM. In this paper, we report on a case study of the Australian government’s “Robodebt” programme that was designed to automatically calculate and collect welfare overpayment debts from citizens but ended up causing severe distress to citizens and welfare agency staff. Employing perspectives from systems thinking and organisational limits, we develop a research model that explains how a socially destructive government ADM programme was initiated, sustained, and delegitimized. The model offers a set of generalisable mechanisms that can benefit investigations of ADM’s consequences. Our findings contribute to the literature of unintended consequences of ADM and demonstrate to practitioners the importance of setting up robust governance infrastructures for ADM programmes.

Suggested Citation

  • Tapani Rinta-Kahila & Ida Someh & Nicole Gillespie & Marta Indulska & Shirley Gregor, 2022. "Algorithmic decision-making and system destructiveness: A case of automatic debt recovery," European Journal of Information Systems, Taylor & Francis Journals, vol. 31(3), pages 313-338, May.
    • Handle: RePEc:taf:tjisxx:v:31:y:2022:i:3:p:313-338
    DOI: 10.1080/0960085X.2021.1960905
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/0960085X.2021.1960905
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/0960085X.2021.1960905?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 search for a different version of it.

    More about this item

    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:taf:tjisxx:v:31:y:2022:i:3:p:313-338. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjis .

    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.