The Relationship Between Artificial Intelligence and Poverty from a Macroeconomic Perspective: Global Opportunities, Risks, and Policy Recommendations

Year 2025, Volume: 10 Issue: 2, 95 - 105

Yunus Budak

Abstract

This article examines the multidimensional relationship between artificial intelligence (AI) technologies and poverty at the global level from a macroeconomic perspective. Poverty is not only a lack of income but also includes elements such as social exclusion, inadequate access to services, and digital inequality. The article explores the dual potential of AI to both alleviate and deepen poverty. Alongside the positive impacts such as improved service quality, digital inclusivity, and greater transparency in governance, risks such as unemployment, algorithmic bias, and digital divide are also being analyzed. The study argues that these opposing effects must be managed through human-centered and holistic policy approaches, and it offers recommendations based on investments in digital infrastructure, skills transformation, social protection systems, and ethical regulations.

Keywords

Artificial intelligence , poverty , digital inequality , inclusive development

Makro İktisadi Perspektiften Yapay Zekâ ve Yoksulluk İlişkisi Küresel Fırsatlar, Riskler ve Politika Önerileri

Abstract

Bu makale, yapay zekâ teknolojilerinin küresel düzeyde yoksullukla olan çok boyutlu ilişkisini makro iktisat perspektifinden incelemektedir. Yoksulluk; sadece gelir eksikliği değil, aynı zamanda sosyal dışlanma, hizmetlere erişim yetersizliği ve dijital eşitsizlik gibi unsurları da içermektedir. Yapay zekânın yoksulluğu hem azaltma hem de derinleştirme potansiyeli ele alınmaktadır. Bununla birlikte, hizmet kalitesinin artması, dijital kapsayıcılık ve yönetişimde şeffaflık gibi olumlu etkilerle birlikte; işsizlik, algoritmik önyargı ve dijital bölünme gibi riskler de analiz edilmektedir. Makale, bu ikili etkinin insan merkezli ve bütüncül politikalarla yönetilmesi gerektiğini savunmakta; dijital altyapı, beceri dönüşümü, sosyal güvenlik ve etik düzenlemelere dayalı öneriler sunmaktadır.

Keywords

Yapay zekâ , yoksulluk , dijital eşitsizlik , kapsayıcı kalkınma

References

• Acemoğlu, D. (2021). Redesigning AI: Work, democracy and justice in the age of automation. In K. Crawford, R. Calo, & R. Lee (Eds.), Boston Review Forum: Redesigning AI. Boston Review.
• Acemoğlu, D., & Restrepo, P. (2022). Tasks, automation, and the rise in US wage inequality. Econometrica, 90(5), 1973–2016.
• Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Review Press.
• Aijaz, N., Lan, H., Raza, T., Yaqub, M., Iqbal, R., & Pathan, M. S. (2025). Artificial intelligence in agriculture: Advancing crop productivity and sustainability. Journal of Agriculture and Food Research, 20, 101762.
• Aiken, E., Bellue, M., Karlan, D., Udry, C., & Blumenstock, J. (2022). Machine learning and phone data can improve targeting of humanitarian aid. Nature, 603(7903), 864–870.
• Alonso, C., Kothari, S., & Rehman, S. (2020). How artificial intelligence could widen the gap between rich and poor nations (IMF Working Paper No. 2020/184). International Monetary Fund.
• Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias: There’s software used across the country to predict future criminals. And it’s biased against Blacks. ProPublica.
• Blumenstock, J. E., Cadamuro, G., & On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), 1073–1076.
• Boston Consulting Group (BCG). (2023). South Africa and artificial intelligence: The potential impact of AI and generative AI across healthcare, education, financial inclusion, and agriculture.
• Bresnahan, T. F., & Trajtenberg, M. (1995). General purpose technologies: “Engines of growth”? Journal of Econometrics, 65(1), 83–108.
• Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
• Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.
• Cazzaniga, M., Jaumotte, F., Li, L., Melina, G., Panton, A. J., Pizzinelli, C., Rockall, E. J., & Mendes Tavares, M. (2024). Gen-AI: Artificial intelligence and the future of work (IMF Staff Discussion Note No. 2024/001). International Monetary Fund.
• Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
• European Commission. (2021). Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act).
• Georgieva, K. (2024). AI will transform the global economy: Let’s make sure it benefits humanity. IMF Blog.
• International Monetary Fund. (2024). AI preparedness index and policy roadmap.
• Jack, W., & Suri, T. (2014). Risk sharing and transactions costs: Evidence from Kenya’s mobile money revolution. American Economic Review, 104(1), 183–223.
• Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790–794.
• Mokyr, J. (1990). The lever of riches: Technological creativity and economic progress. Oxford University Press.
• Muralidharan, K., Singh, A., & Ganimian, A. J. (2019). Disrupting education? Experimental evidence on technology-aided instruction in India. American Economic Review, 109(4), 1426–1460.
• Nah, F. F. H., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI–human collaboration. Journal of Information Technology Case and Application Research, 25(2), 1–28.
• NITI Aayog. (2021). Responsible AI for all: National strategy for artificial intelligence. Government of India.
• Nose, M., Pierri, N., & Honda, J. (2025). Leveraging digital technologies in boosting tax collection (IMF Working Paper No. 25/89). International Monetary Fund, Fiscal Affairs Department.
• Oxford Economics. (2019). How robots change the world: What automation really means for jobs and productivity.
• Sen, A. (1999). Development as freedom. Alfred A. Knopf.
• Susskind, D. (2020). A world without work: Technology, automation, and how we should respond. Metropolitan Books.
• United Nations Development Programme. (2024). Human development report 2023/24: Breaking the gridlock – Reimagining cooperation in a polarized world.
• United Nations Development Programme. (2023). Human development report 2023/2024: Breaking the gridlock – Reimagining cooperation in a polarized world.
• United Nations Educational, Scientific and Cultural Organization (UNESCO). (2022). Cracking the code: Girls’ and women’s education in STEM. UNESCO Publishing.
• United Nations Industrial Development Organization (UNIDO). (2022). AI in manufacturing for inclusive and sustainable industrial development.
• Whittaker, M., Crawford, K., Dobbe, R., Fried, G., Kaziunas, E., Mathur, V., & West, S. M. (2018). AI Now Report 2018. AI Now Institute.
• World Health Organization. (2022). Global Health Observatory data repository.
• World Bank. (2022). Digital technologies in economic transformation. World Bank Publications.
• World Economic Forum. (2024). After a childhood surrounded by conflict, this social entrepreneur is helping vulnerable communities access legal support.
• World Inequality Lab. (2022). World inequality report 2022.
• Yeh, C., Perez, A., Driscoll, A., Azzari, G., Tang, Z., Lobell, D., Ermon, S., & Burke, M. (2020). Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. Nature Communications, 11, 2583.