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.

This study investigates perceptions of poverty and its underlying determinants among a purposively sampled population of social-benefit recipients in Uzbekistan. For this purpose, a survey was conducted with 1949 respondents who receive social benefits. The collected data were analyzed using an exploratory sequential mixed-methods approach. Initially, thematic analysis was conducted to identify perceptions of poverty. The output of the thematic analysis was the emergence of nine themes that illustrate the characteristics of individualistic, structural, fatalistic, and cultural attributions of poverty perceptions. Based on the output of the thematic analysis, multinomial logistic regression was conducted. The selection of the base category was based on both the theoretical framework and survey output. The regression results indicated that age, regional poverty cluster and place of residence were the most statistically significant demographic factors influencing perceptions of poverty. Among the monetary variables, personal income demonstrated the strongest explanatory power. Overall, the findings suggest that, within this sampled population, perceptions of poverty are predominantly shaped by individualistic attributions rather than structural, fatalistic or cultural ones. However, these results should be considered with caution as they may reflect internalized poverty stigma.