Harnessing AI to Combat Poverty: Innovative Approaches
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Chapter 1: Understanding Poverty through AI
Despite significant advancements in living standards across numerous Western nations, poverty remains a critical challenge in various regions globally. Approximately 9% of the population lives in extreme poverty, surviving on less than $2.15 a day. This definition focuses on monetary poverty, where individuals are deemed impoverished if they cannot afford basic living expenses. However, poverty can also be assessed through a non-monetary lens, considering factors such as access to education, healthcare, and opportunities.
Identifying and predicting poverty is crucial for enhancing the effectiveness of poverty alleviation efforts, including foreign aid. Traditional methods typically rely on economic indicators, such as income and expenditure, while non-monetary poverty assessments often use survey and census data. Yet, given the multifaceted nature of non-monetary poverty, exploring additional relevant variables is essential.
AI presents a promising solution for improving poverty recognition. Its advantages over traditional models include the ability to process a larger number of parameters, increased accuracy, faster computation, and reduced human input. Moreover, AI excels in identifying significant variables within diverse datasets, including remote sensing data, call records, and online shopping behavior. Utilizing a broader range of data allows for more detailed and frequently updated insights, aiding in poverty identification and support.
AI's ability to analyze diverse data sets can reveal alternative indicators of poverty.
Monetary Poverty Recognition
AI can assist in identifying monetary poverty by analyzing various datasets that reflect financial conditions. This analysis can occur at different levels:
- Individual Level: At this level, AI can gauge individual perceptions of poverty by examining text data from interviews and surveys. By processing these texts, AI can extract sentiments related to poverty, its implications, and the perceived causes, employing techniques such as:
- Breaking down text into manageable segments.
- Identifying root forms of words.
- Analyzing grammatical structures to determine relationships among terms.
- Household Level: AI can also assess monetary poverty by analyzing data from the bottom 40% (B40) of income earners. By sifting through extensive datasets, AI can pinpoint characteristics that define these households, such as:
- Geographic location.
- Ethnicity.
- Household size.
- Total and average income.
- Community Level: At this level, AI can examine urban poverty by analyzing geospatial data and survey information to understand population density and infrastructure. This analysis can help identify slum conditions, with related factors including:
- Ethnicity and religion.
- Region.
- High fertility rates.
- Low school attendance.
- City Level: AI can further assess monetary poverty at the city level by analyzing extensive e-commerce datasets. For instance, a comprehensive dataset containing information on millions of advertisements can reveal that certain goods, like cars and motorbikes, are strong indicators of poverty.
Non-Monetary Poverty Recognition
AI's capabilities extend beyond monetary poverty, allowing for the assessment of non-monetary indicators as well. This can also be done at multiple levels:
- Household Level: AI can evaluate household poverty through survey data encompassing demographics and health metrics. Key indicators include:
- The number and age of household members.
- Accessibility to essential resources, like clean water.
- City Level: At the village level, AI can utilize satellite imagery and geographic data to identify poverty indicators. Three critical dimensions for evaluation include:
- Socioeconomic conditions (e.g., land use).
- Access to services (e.g., proximity to healthcare).
- Agricultural conditions (e.g., land use for crops).
- County Level: AI can analyze nighttime satellite images to detect variations in light usage, which can indicate poverty levels based on pixel data characteristics.
- Country Level: Non-monetary poverty can also be assessed through indicators of malnutrition and asset poverty, utilizing historical data and geographical features. Notably, factors such as the number of underweight women are significant indicators.
Conclusion
In summary, AI provides a robust framework for recognizing both monetary and non-monetary poverty through diverse and expansive datasets. This technology enables the discovery of new indicators that can be applied across various data contexts, enhancing poverty recognition at individual, household, community, city, county, and national levels.
How to Take Action
Here are some actionable steps individuals can take to help alleviate poverty:
- Share your knowledge and skills to empower those with lower incomes.
- Contribute to charitable organizations.
- Support local businesses.
- Volunteer with groups dedicated to assisting low-income communities.
- Educate yourself on issues like elderly poverty to foster prevention strategies.
Which of these actions can you integrate into your daily routine? Do you have additional suggestions for combating poverty? Your thoughts and contributions are invaluable, so please share them in the comments below to motivate others.
Further Reading
- How Artificial Intelligence Makes Recycling Easier
- How Artificial Intelligence Can Be Used To Reach A Brighter Future
Credit
This article is based on:
Usmanova, A., Aziz, A., Rakhmonov, D., & Osamy, W. (2022). Utilities of Artificial Intelligence in Poverty Prediction: A Review. Sustainability, 14(21), 14238.