IDEAS home Printed from https://ideas.repec.org/a/spr/trosos/v17y2023i2d10.1007_s12626-023-00146-y.html
   My bibliography  Save this article

Role of Regulatory Sandboxes and MLOps for AI-Enabled Public Sector Services

Author

Listed:
  • Ana Paula Gonzalez Torres

    (Aalto University)

  • Nitin Sawhney

    (Aalto University)

Abstract

This paper discusses how innovations in public sector AI-based services must comply with the Artificial Intelligence Act (AI Act) regulatory frameworks while enabling experimentation and participation of diverse stakeholders throughout the Artificial Intelligence (AI) lifecycle. The paper examines the implications of the emerging regulation, AI regulatory sandboxes and Machine Learning Operations (MLOps) as tools that facilitate compliance while enabling co-learning and active participation of multiple stakeholders. We propose a framework that fosters experimentation with automation pipelines and continuous monitoring for the deployment of future public sector AI-based services in a regulatory-compliant and technically innovative manner. AI regulatory sandboxes can be beneficial as a space for contained experimentation that goes beyond regulatory considerations to specific experimentation with the implementation of ML frameworks. While the paper presents a framework based on emerging regulations, tools and practices pertaining to the responsible use of AI, this must be validated through pilot experimentation with public and private stakeholders and regulators in different areas of high-risk AI-based services.

Suggested Citation

  • Ana Paula Gonzalez Torres & Nitin Sawhney, 2023. "Role of Regulatory Sandboxes and MLOps for AI-Enabled Public Sector Services," The Review of Socionetwork Strategies, Springer, vol. 17(2), pages 297-318, October.
    • Handle: RePEc:spr:trosos:v:17:y:2023:i:2:d:10.1007_s12626-023-00146-y
    DOI: 10.1007/s12626-023-00146-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12626-023-00146-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12626-023-00146-y?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.

    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:spr:trosos:v:17:y:2023:i:2:d:10.1007_s12626-023-00146-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.