Artificial Intelligence and Corporate Social Responsibility: A Systematic Review of Emerging Integration, Mechanisms, and Challenges

This study examines the emerging integration of artificial intelligence (AI) into corporate social responsibility (CSR) through a systematic literature review covering 2013–2024. Drawing on PRISMA‐guided screening, thematic coding, and bibliometric mapping, the review identifies five recurrent empirical clusters: sustainability and resource optimization, ethical governance, transparency and accountability, stakeholder engagement, and CSR innovation. Rather than treating these clusters as the primary contribution, the study develops a mechanism‐based explanation of AI–CSR relationships. Specifically, the review highlights three recurring generative mechanisms: datafication and auditing, algorithmic mediation of responsibility, and stakeholder salience reweighting. These mechanisms help explain how AI can improve CSR performance, legitimacy, and responsiveness under specific boundary conditions, while also generating risks such as assurance gaps, accountability diffusion, stakeholder exclusion, and metric crowding‐out. The bibliometric evidence suggests increasing topical proximity and methodological coupling between AI and CSR but only limited evidence of full theoretical integration. Accordingly, the paper reframes the field as an emerging area of integration rather than a completed convergence. The study concludes by offering a more cautious theoretical synthesis, a set of aligned propositions, and an agenda for future research on the responsible use of AI in CSR governance and practice.