Exploring Scalable Multimodal Approaches
AI/ML techniques can generate unbiased geographical data and identify areas of extreme poverty, which policymakers can adopt to prioritize development aid allocations. This innovative method can help develop scalable algorithms to inform humanitarian responses to the Republic of Congo's food crisis.
Big Data &
In a world riddled with poverty, data scarcity hampers policy maker’s ability to deliver aid to those most in need and assess the intervention from the perspective of its distributive role. There is no reason why the aid industry, one in which human lives are at stake, fails to adopt the technologies applied in other fields.
Creating accurate indices of extreme poverty is an important ethical, empirical, and policy-relevant consideration. Credible and reliable measures of poverty are powerful instruments in the drive toward greater justice and toward securing the rights of all individuals to a decent standard of living. The ways in which poverty is measured shape perceptions of need and have repercussions for targeting interventions.
Nari Gunjan’s work with the Musahars of Bihar has been an exemplary case of a women’s empowerment program that has targeted an extremely needy group. As the case study has revealed, the significant increases in literacy rate among girls at the NG centers and awareness of their well being is a testament to the important contributions NG has made to the Musahar community.