Utilities of Artificial Intelligence in Poverty Prediction: A Review

Aziza Usmanova (), Ahmed Aziz, Dilshodjon Rakhmonov and Walid Osamy
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Aziza Usmanova: International Business Management Department, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
Ahmed Aziz: International Business Management Department, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
Dilshodjon Rakhmonov: International Business Management Department, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
Walid Osamy: Computer Science Department, Faculty of Computer Science and Artificial Intelligence, Benha University, Banha 13511, Egypt

Sustainability, 2022, vol. 14, issue 21, 1-39

Abstract: Artificial Intelligence (AI) is generating new horizons in one of the biggest challenges in the world’s society—poverty. Our goal is to investigate utilities of AI in poverty prediction via finding answers to the following research questions: (1) How many papers on utilities of AI in poverty prediction were published up until March, 2022? (2) Which approach to poverty was applied when AI was used for poverty prediction? (3) Which AI methods were applied for predicting poverty? (4) What data were used for poverty prediction via AI? (5) What are the advantages and disadvantages of the created AI models for poverty prediction? In order to answer these questions, we selected twenty-two papers using appropriate keywords and the exclusion criteria and analyzed their content. The selection process identified that, since 2016, publications on AI applications in poverty prediction began. Results of our research illustrate that, during this relatively short period, the application of AI in predicting poverty experienced a significant progress. Overall, fifty-seven AI methods were applied during the analyzed span, among which the most popular one was random forest. It was revealed that with the adoption of AI tools, the process of poverty prediction has become, from one side, quicker and more accurate and, from another side, more advanced due to the creation and possibility of using different datasets. The originality of this work is that this is the first sophisticated survey of AI applications in poverty prediction.

Keywords: artificial intelligence; poverty; machine learning; deep learning; remote sensing; wealth (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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