Targeting Humanitarian Aid with Machine Learning and Digital Data

Explore how machine learning and digital data can revolutionize humanitarian aid targeting in a thought-provoking talk by Emily Aiken from UC Berkeley. Delve into the challenges of allocating aid in low- and middle-income countries, where limited data on poverty and vulnerability often hinder effective distribution. Discover how innovative "big" digital data sources, including satellite imagery, mobile phone data, and financial service provider information, combined with advanced machine learning techniques, can enhance the accuracy of aid program targeting. Examine empirical results from case studies in Togo and Bangladesh, showcasing the potential of these data-driven and algorithmic approaches. Consider the broader implications of these methods on fairness, privacy, transparency, and community dynamics in humanitarian aid allocation. Gain insights from Aiken's research as a PhD candidate at UC Berkeley's School of Information, where she focuses on applying novel algorithms and digital data sources to social protection programs.

Targeting humanitarian aid with machine learning and digital data—Emily Aiken (Berkeley)