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

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.