AI in International Social Work and Global Issues

Background and Purpose:
Artificial intelligence (AI) is increasingly being applied to address urgent global challenges in international social work, such as migration, forced displacement, disaster response, and public health crises. This study explores how AI-driven tools—including predictive modeling, automated decision support, and intelligent communication systems—are reshaping humanitarian practice. At the same time, it highlights the importance of cultural competence and ethical collaboration, particularly when these technologies are deployed across diverse sociocultural and geopolitical contexts. Emphasizing the need for decolonizing frameworks and Indigenous knowledge integration, the study critically examines both the promises and challenges of AI. It ultimately advocates for responsible, community-centered AI development grounded in justice, inclusivity, and global equity.

Methods:
Using an interdisciplinary framework, the study draws on empirical studies, real-world applications, and policy developments to analyze AI’s role across six major domains: (1) migration and refugee management; (2) humanitarian aid and disaster response; (3) global health crises; (4) ethical and culturally competent AI collaboration; (5) risks and limitations of AI—including algorithmic bias, privacy threats, and the digital divide; and (6) future directions for responsible AI development in international social work. Cultural humility, decolonizing data, and participatory design are applied as guiding concepts to assess the implications of AI adoption in global contexts.

Results:
AI has shown significant utility in refugee tracking, early disaster warning systems, and public health surveillance during global emergencies. However, its application in international social work raises concerns. Biased training data have resulted in discriminatory algorithms, while data extraction practices often lack transparency or consent. Inadequate infrastructure and governance in low-resource settings further compound these risks, reinforcing global inequities. Additionally, many AI systems are not designed with cultural contexts in mind, limiting their relevance and impact. The study identifies strategies for addressing these issues, including ethical governance, cross-sector collaboration, and inclusive design that centers community voices.

Conclusion and Implication:
While AI holds great promise for advancing international social work, its benefits must not come at the expense of cultural integrity, equity, and human dignity. Responsible AI requires not only technical rigor but also a commitment to ethical principles and community partnership. The study calls for stronger ethical guidelines, culturally competent practices, and capacity building for international social workers. It emphasizes that future AI systems must be co-developed with affected communities, include Indigenous knowledge systems, and be subject to robust oversight. By prioritizing inclusive, decolonized, and participatory approaches, international social work can harness AI’s potential while safeguarding the values that underpin the profession.