IDEAS home Printed from https://ideas.repec.org/a/gam/jscscx/v14y2025i1p30-d1564568.html
   My bibliography  Save this article

Democratizing Artificial Intelligence for Social Good: A Bibliometric–Systematic Review Through a Social Science Lens

Author

Listed:
  • Chitat Chan

    (Department of Social Work, Hong Kong Baptist University, 15 Baptist University Road, Kowloon Tong, KLN, Hong Kong)

  • Afifah Nurrosyidah

    (Institute of Information Management, National Cheng Kung University, No.1, University Road, Tainan City 701401, Taiwan)

Abstract

This study provides a comprehensive analysis of the opportunities for democratizing artificial intelligence (AI) for social good using a bibliometric–systematic literature review method. It combines the quantitative analysis of bibliometric methods with the qualitative synthesis of systematic reviews. This approach helps identify patterns, trends, and gaps in the literature, advancing theoretical insights and mapping future research directions. Design/methodology/approach: Scopus, PubMed, and Web of Science, as prominent scientific databases, were utilized to examine publications between 2014 and 2024. The article selection followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The bibliometric analysis was conducted using CiteSpace software. Findings: The bibliometric analysis identified the most influential articles, journals, countries, authors, and key themes. The systematic thematic analysis identified established modes of using AI for social good. Moreover, future research directions are suggested and discussed in this article. Practical implications: The findings give future research directions and guidance to academics, practitioners, and policymakers for real-world applications.

Suggested Citation

  • Chitat Chan & Afifah Nurrosyidah, 2025. "Democratizing Artificial Intelligence for Social Good: A Bibliometric–Systematic Review Through a Social Science Lens," Social Sciences, MDPI, vol. 14(1), pages 1-27, January.
  • Handle: RePEc:gam:jscscx:v:14:y:2025:i:1:p:30-:d:1564568
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2076-0760/14/1/30/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2076-0760/14/1/30/
    Download Restriction: no
    ---><---

    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:gam:jscscx:v:14:y:2025:i:1:p:30-:d:1564568. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.