ALGORITHMIC FAIRNESS IN BUSINESS AND SOCIAL SCIENCES: A BIBLIOMETRIC REVIEW OF EMERGING ETHICAL AI GOVERNANCE RESEARCH

Abstract

The rapid adoption of artificial intelligence (AI) and algorithmic decision-making systems in business, management, and public governance has intensified scholarly attention toward algorithmic fairness, ethical accountability, and responsible AI governance. Despite the growing volume of research, the intellectual structure, thematic evolution, and interdisciplinary integration of algorithmic fairness studies remain fragmented across disciplinary boundaries. This study aims to systematically map the development of algorithmic fairness research within the fields of Business, Management and Accounting, Decision Sciences, and Social Sciences using bibliometric analysis and network visualization techniques. Using Scopus-indexed publications published between 2020 and 2026, this study analyzes publication trends, citation structures, institutional productivity, country contributions, subject-area distributions, and thematic keyword relationships. Bibliometric indicators such as total publications, total citations, h-index, and g-index were employed to assess scholarly impact, while VOSviewer was utilized to construct keyword co-occurrence and thematic network visualizations. The findings reveal a significant increase in scholarly attention toward algorithmic fairness, particularly after 2022, driven by the emergence of ethical AI governance, fairness-aware machine learning, and responsible digital transformation initiatives. The United States dominates global research output, while interdisciplinary collaboration between computer science, decision sciences, and business management has intensified. Network visualization demonstrates that fairness, governance, bias mitigation, accountability, AI ethics, and predictive analytics form the core intellectual clusters within the literature. Emerging themes include governance-aware AI systems, fairness in human resource analytics, privacy-preserving machine learning, and ethical algorithm deployment in financial services. This study contributes theoretically by integrating fragmented streams of algorithmic fairness literature into a unified managerial and governance-oriented perspective. Practically, the findings provide strategic insights for organizations, policymakers, and researchers seeking to design ethically responsible AI systems and governance frameworks. The study also identifies future research opportunities related to explainable AI, fairness auditing, sustainability-oriented AI governance, and cross-cultural algorithmic accountability.