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Neil Buddy Shah, CEO of the Clinton Health Access Initiative and former managing director of GiveWell, discusses innovative solutions for data scarcity in low-income countries, the impact of economic growth and governance on public health, the decline in HIV/AIDS lethality, and the Forbes Under 30 list.
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- Machine learning algorithms can help gather specific data in low-income countries, allowing targeted interventions for social and medical aid.
- AI and machine learning have the potential to revolutionize public health, but data scarcity and privacy concerns must be addressed.
Deep dives
Innovative Solutions for Gathering Data in Low-Income Countries
Data scarcity poses challenges for decision makers in low-income countries. Innovative solutions like basic machine learning algorithms have been used to gather specific data. For example, in Northern India, a machine learning model was developed to identify villages with a high density of out-of-school girls, allowing targeted interventions to re-enroll and educate them. Additionally, low-cost survey tools involving local individuals have been employed to collect data on various indicators like teacher absenteeism and health facility wait times.
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