Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Targeting Humanitarian Aid with Machine Learning and Digital Data

Paul G. Allen School via YouTube

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
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.

Syllabus

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

Taught by

Paul G. Allen School

Reviews

Start your review of Targeting Humanitarian Aid with Machine Learning and Digital Data

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.