International Aid in the Era of Artificial Intelligence: Potential Advantages, Theoretical Challenges, and Strategic Responses

Xin Wang; Quanzhi Lu

doi:10.62177/amit.v1i6.887

Authors

Xin Wang Guangzhou College of Commerce
Quanzhi Lu Guangzhou College of Commerce

DOI:

https://doi.org/10.62177/amit.v1i6.887

Keywords:

Artificial Intelligence, International Aid, Data Governance, Algorithmic Bias, Digital Divide, Global Cooperation

Abstract

This paper explores the transformative impact of artificial intelligence (AI) on international aid. It systematically analyzes AI's potential to enhance efficiency in needs assessment, resource allocation, project implementation, and monitoring. However, the integration of AI introduces profound theoretical challenges, including data governance dilemmas, algorithmic bias, the digital divide, sovereignty risks, and value conflicts. In response, the study proposes a strategic framework grounded in global governance, emphasizing data ethics, algorithmic accountability, local capacity building, and international cooperation. This study aims to provide a strategic framework for the responsible and equitable deployment of AI in international aid, ultimately serving humanitarian and sustainable development goals.

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References

Rao, D. T., Sethi, N., Dash, D. P., & Bhujabal, P. (2023). Foreign aid, FDI and economic growth in South-East Asia and South Asia. Global Business Review, 24(1), 31-47.

Mahembe, E., & Odhiambo, N. M. (2021). Does foreign aid reduce poverty? A dynamic panel data analysis for sub-Saharan African countries. The Journal of Economic Inequality, 19(4), 875-893.

Khan, R., Zeeshan, Haque, M. I., Gupta, N., Tausif, M. R., & Kaushik, I. (2022). How foreign aid and remittances affect poverty in MENA countries?. Plos one, 17(1), e0261510.

Toetzke, M., Banholzer, N., & Feuerriegel, S. (2022). Monitoring global development aid with machine learning. Nature Sustainability, 5(6), 533-541.

Shahriar, S., Allana, S., Hazratifard, S. M., & Dara, R. (2023). A survey of privacy risks and mitigation strategies in the artificial intelligence life cycle. IEEE Access, 11, 61829-61854.

Bircan, T., & Özbilgin, M. F. (2025). Unmasking inequalities of the code: Disentangling the nexus of AI and inequality. Technological Forecasting and Social Change, 211, 123925.

Gardner, C., & Henry, P. B. (2023). The global infrastructure gap: Potential, perils, and a framework for distinction. Journal of Economic Literature, 61(4), 1318-1358.

Beduschi, A. (2022). Harnessing the potential of artificial intelligence for humanitarian action: Opportunities and risks. International Review of the Red Cross, 104(919), 1149-1169.

Tan, L., Guo, J., Mohanarajah, S., & Zhou, K. (2021). Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices. Natural Hazards, 107(3), 2389-2417.

Rehan, H. (2022). Enhancing Disaster Response Systems: Predicting and Mitigating the Impact of Natural Disasters Using AI. Journal of Artificial Intelligence Research, 2(1), 501.

Chen, W., Men, Y., Fuster, N., Osorio, C., & Juan, A. A. (2024). Artificial intelligence in logistics optimization with sustainable criteria: A review. Sustainability, 16(21), 9145.

Goralski, M. A., & Tan, T. K. (2022). Artificial intelligence and poverty alleviation: Emerging innovations and their implications for management education and sustainable development. The International Journal of Management Education, 20(3), 100662.

Bhatt, P., Liu, J., Gong, Y., Wang, J., & Guo, Y. (2022). Emerging artificial intelligence–empowered mhealth: scoping review. JMIR mHealth and uHealth, 10(6), 35053.

Pillai, M., Griffin, A. C., Kronk, C. A., & McCall, T. (2023). Toward community-based natural language processing (CBNLP): cocreating with communities. Journal of Medical Internet Research, 25, 48498.

Chen, K., Zhou, X., Lin, Y., Feng, S., Shen, L., & Wu, P. (2025). A survey on privacy risks and protection in large language models. Journal of King Saud University Computer and Information Sciences, 37(7), 163.

Fountain, J. E. (2022). The moon, the ghetto and artificial intelligence: Reducing systemic racism in computational algorithms. Government Information Quarterly, 39(2), 101645.

Zajko, M. (2022). Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates. Sociology Compass, 16(3), 12962.

Fink, K. (2018). Opening the government’s black boxes: freedom of information and algorithmic accountability. Information, Communication & Society, 21(10), 1453-1471.

Salami, A. O. (2024). Artificial intelligence, digital colonialism, and the implications for Africa’s future development. Data & Policy, 6, 67.

Kamila, M. K., & Jasrotia, S. S. (2025). Ethical issues in the development of artificial intelligence: recognizing the risks. International Journal of Ethics and Systems, 41(1), 45-63.