Authors : George Kimwomi; Mvurya Mgala

Volume/Issue : Volume 9 - 2024, Issue 9 - September

Google Scholar : https://tinyurl.com/2m4b95k5

Scribd : https://tinyurl.com/2atpuxvc

DOI : https://doi.org/10.38124/ijisrt/IJISRT24SEP312

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Extreme poverty is among the challenges the United Nations seeks to eradicate by the year 2030 as outlined in its Sustainable Development Goals. However, governments and other stakeholders face challenges in accurately identifying poverty in households for evidence- based implementation of SDG programs. Current strategies are slow, inaccurate and costly to efficiently support efforts to identify poverty for sustainable development. Consequently, many strategies to map out poverty for intervention measures do not succeed which could be contributing to the global decline in the rate of reducing poverty. Artificial intelligence which has become widely available and has been used in many sectors, could be leveraged to improve poverty mapping for evidence-based interventions for sustainable development. Despite living in the era of AI, it has not been fully utilized in mapping poverty. This review seeks to explore the extent of research on the adoption of AI in mapping poverty so as to find the gap for further research. It aims to establish the extent of AI-based research on identification of poverty in respect to global distribution of research studies, methods, algorithms and sources of data which have been used in studies to identify poverty. The findings will help to identify gaps for research to help in designing evidence-based strategies for intervention measures. A systematic review was done for the period 2020 to 2024 using databases and snowballing hybrid search approach. A qualitative analysis was done on the extracted data to uncover new patterns and identify research gaps.

Keywords : Artificial Intelligence, Poverty Classification, Sustainable Development Goals.

References :

1. Agyemang, F. S., Memon, R., Wolf, L. J., & Fox, S. (2023). High-resolution rural poverty mapping in Pakistan with ensemble deep learning. Plos One, 18(4), e0283938.
2. Alfawzan, A. (2022). IDENTIFICATION OF POVERTY AREAS BY USING MACHINE LEARNING CLASSIFICATION METHODS FROM SA℡LITE IMAGERY IN BURAYDAH CITY, IN THE QASSIM REGION OF SAUDI ARABIA. https://digitalcommons.murraystate.edu/etd/239/
3. Alsharkawi, A., Al-Fetyani, M., Dawas, M., Saadeh, H., & Alyaman, M. (2021). Poverty classification using machine learning: The case of Jordan. Sustainability, 13(3), 1412.
4. Anuoluwapo, D., & Uwizeyimana, D. (2021). Achieving Sustainable Development Goal 1 in Ogun State, Nigeria: Lessons from the Millennium Development Goals Poverty Reduction Strategy. African Journal of Development Studies (Formerly AFFRIKA Journal of Politics, Economics and Society), 11(1), 79–105. https://doi.org/10.31920/2634-3649/2021/v11n1a4
5. Bank, W. (2020). Poverty and shared prosperity 2020: Reversals of fortune. The World Bank. https://www.im4change.org/upload/files/Poverty%20and%20Shared%20Prosperity%202020%20Reversals%20of%20Fortune%20World%20Bank%20Group.pdf
6. Eshiotse, E., Jeremiah, A., Bisong, B. D., Ofem, N. O., Uyang, F. A., Odinka, G. E., Abang, P. O., Undelikwo, V. A., Bukie, B. F., Wilson, N. U., & Okpa, J. T. (2023). Exploring factors affecting government delivery of social welfare services using a multi-method approach. Corporate Governance and Organizational Behavior Review, 7(2), 26–37. https://doi.org/10.22495/cgobrv7i2p3
7. Ferreira, M. B., Pinto, D. C., Herter, M. M., Soro, J., Vanneschi, L., Castelli, M., & Peres, F. (2021). Using artificial intelligence to overcome over-indebtedness and fight poverty. Journal of Business Research, 131, 411–425.
8. Fisker, P., & Mdadila, K. (2022). Urban Poverty Mapping with Open Spatial Data: Evidence from Dar es Salaam. Available at SSRN 4305838. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4305838
9. Gao, C., Fei, C. J., McCarl, B. A., & Leatham, D. J. (2020). Identifying Vulnerable households using machine learning. Sustainability, 12(15), 6002.
10. Hersh, J., Engstrom, R., & Mann, M. (2021). Open data for algorithms: Mapping poverty in Belize using open satellite derived features and machine learning. Information Technology for Development, 27(2), 263–292. https://doi.org/10.1080/02681102.2020.1811945
11. Hofer, M., Sako, T., Martinez Jr, A., Addawe, M., Bulan, J., Durante, R. L., & Martillan, M. (2020). Applying artificial intelligence on satellite imagery to compile granular poverty statistics. Asian Development Bank Economics Working Paper Series, 629. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3785112
12. Hossain, M. E., Kabir, M. A., Zheng, L., Swain, D. L., McGrath, S., & Medway, J. (2022). A systematic review of machine learning techniques for cattle identification: Datasets, methods and future directions. Artificial Intelligence in Agriculture, 6, 138–155. https://doi.org/10.1016/j.aiia.2022.09.002
13. Hu, S., Ge, Y., Liu, M., Ren, Z., & Zhang, X. (2022). Village-level poverty identification using machine learning, high-resolution images, and geospatial data. International Journal of Applied Earth Observation and Geoinformation, 107, 102694.
14. Isnin, R., Bakar, A. A., & Sani, N. S. (2020). Does Artificial Intelligence Prevail in Poverty Measurement? Journal of Physics: Conference Series, 1529(4), 042082. https://iopscience.iop.org/article/10.1088/1742-6596/1529/4/042082/meta
15. Jiang, P. (2022). Identification of Poor Households in Precision Poverty Alleviation Based on Ensemble Learning. 7–10. https://doi.org/10.1109/CCAT56798.2022.00009
16. Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424.
17. Maruejols, L., Wang, H., Zhao, Q., Bai, Y., & Zhang, L. (2023). Comparison of machine learning predictions of subjective poverty in rural China. China Agricultural Economic Review, 15(2), 379–399.
18. Mvurya, M. (2020). The Extent and Use of Artificial Intelligence to Achieve the Big Four Agenda in Kenya. Multidisciplinary Journal of Technical University of Mombasa, 1(1), Article 1. https://doi.org/10.48039/mjtum.v1i1.9
19. Nigus, M., & Shashirekha, H. L. (2022). A comparison of machine learning and deep learning models for predicting household food security status. IJEER, 10(2), 308–311.
20. Nzelibe, I. U. (2024). Satellite and artificial intelligence in mapping multidimensional poverty in Africa. African Journal on Land Policy and Geospatial Sciences, 7(1), 325–347.
21. Research Institute (Ifpri), I. F. P. (2018). Heterogeneity in target populations and locations: Reflections on the challenges for poverty targeting (0 ed.). International Food Policy Research Institute. https://doi.org/10.2499/9780896295988_09
22. United Nations Department of Economic and Social Affairs. (2023). The Sustainable Development Goals Report 2023: Special Edition. United Nations. https://doi.org/10.18356/9789210024914
23. Usmanova, A., Aziz, A., Rakhmonov, D., & Osamy, W. (2022). Utilities of artificial intelligence in poverty prediction: A review. Sustainability, 14(21), 14238.
24. Wohlin, C., Kalinowski, M., Romero Felizardo, K., & Mendes, E. (2022). Successful combination of database search and snowballing for identification of primary studies in systematic literature studies. Information and Software Technology, 147, 106908. https://doi.org/10.1016/j.infsof.2022.106908
25. Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016). Machine learning in manufacturing: Advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23–45. https://doi.org/10.1080/21693277.2016.1192517
26. Zamzuri, M. H. A., Sofian, N., & Hassan, R. (2023). The Forecasting of Poverty using the Ensemble Learning Classification Methods. International Journal on Perceptive and Cognitive Computing, 9(1), 24–32.
27. Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021). A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity, 2021, 1–18.
28. Zhang, W., Lei, T., Gong, Y., Zhang, J., & Wu, Y. (2022). Using Explainable Artificial Intelligence to Identify Key Characteristics of Deep Poverty for Each Household. Sustainability, 14(16), 9872.