How AI & Mobile Data Can Be Used to Fight Poverty

Yana Khare 25 Apr, 2023 • 3 min read
Using AI and Mobile Data to reduce poverty

A recent study emphasizes the possibility of employing machine learning and data from mobile phones to target and reduce high poverty rates. Mobile devices produce massive volumes of data. Local residents can learn important details about their socioeconomic standing by using them. Tailored programs that aim to eradicate poverty may be created using this information. The paper presents a case study demonstrating how researchers utilized mobile phone data like CDR to identify areas in India with high poverty levels. In addition, the paper also showed how this knowledge was used to develop targeted interventions. The author also emphasizes how machine learning can analyze big datasets & generate insights that can be utilized to reduce poverty.

Also Read: World Bank’s Machine Learning Model to Save Lives in Low-Income Areas

A Promising Solution for Identifying Ultra-Poor Households

Using CDR or call detail records to help combat poverty using AI and machine learning

The author presented the paper at the World Bank and UC Berkeley. Researchers showed how the government’s anti-poverty program could target extremely low-income households successfully. The government could use machine learning on non-traditional administrative data, like call detail records (CDRs) from a significant mobile phone operator in Afghanistan. CDRs include details on, among other things, phone numbers, communication patterns, network contacts, and recharging habits.

A supervised machine learning model trained on CDR data, an asset-based wealth index, and a consumption metric frequently used as a proxy to assess poverty in low- and middle-income countries are all evaluated and compared in this research as three ways of correctly identifying ultra-poor families. The gradient boosting model trained 797 behavioral indicators computed from CDR data for the supervised ML algorithm, outperforming other common machine learning algorithms.

Comparing the Accuracy of the CDR-Based Method and Alternatives to Reduce Poverty

Using CDR through mobile phone to identify people who live in poverty and help reduce it using AI and machine learning

The study discovered that the CDR-based strategy, which achieved precision and recall of 42%, was equally accurate as the other two methods in identifying ultra-poor homes. The results of the area under the curve (AUC) tests also revealed similarities between the techniques. With an AUC of 0.78, the combined approach, which employed logistic regression to classify homes into ultra-poor and non-ultra-poor categories by combining all three methods, outperformed the separate techniques using one or two data sources. However, given the impossibility of gathering consumption data for sizable populations, an eclectic approach employing solely CDR and asset data could be the most practical choice.

Addressing Ethical Concerns and Limitations of Using CDR to Reduce Poverty

Addressing the ethical issues and constraints brought up by using CDR data to reduce poverty is essential. Having access to phone data is vital. If data is unavailable to particular sections of the population, it will affect targeting precision. Additionally, CDR-based targeting entails accessing confidential and sensitive data, calling for detailed privacy requirements and informed permission. Data reduction may be one way to reduce privacy threats, but it may also reduce the effectiveness of targeting. Finally, using CDR data for program eligibility may encourage people who want to game the system to act strategically.

Our Say

Balancing Potential Benefits and Ethical Constraints of Using CDR/ AI/ Machine learning for reducing poverty

Enhancing current survey-based methodologies, merging ML with CDR data, & lowering costs can potentially revolutionize targeting economic interventions or aid programs. Thus, helping in reducing and combating poverty in low-income countries like India. However, we must consider practical and moral considerations, including accessing data, addressing privacy concerns, and preventing possible data manipulation. It is critical to balance these limitations with the potential advantages of CDR-based targeting in each unique situation. It is crucial to approach machine learning applications wisely and responsibly. Additionally, one must ensure that they align with ethical principles and prioritize the welfare of people and communities as technology continues to develop and change the world.

Also Read: Ethics in Data Science and Proper Privacy and Usage of Data

Yana Khare 25 Apr 2023

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