3 citations · 5 across the 4 of their papers we have counts for
8 papers
Can Strategic Data Collection Improve the Performance of Poverty Prediction Models?
Satej Soman, Emily Aiken, Esther Rolf +1
Machine learning-based estimates of poverty and wealth are increasingly being used to guide the targeting of humanitarian aid and the allocation of social assistance. However, the…
Phone Sharing and Cash Transfers in Togo: Quantitative Evidence from Mobile Phone Data
Emily L. Aiken, Viraj Thakur, Joshua E. Blumenstock
Phone sharing is pervasive in many low- and middle-income countries, affecting how millions of people interact with technology and each other. Yet there is very little quantitative…
Gamblers Learn from Experience
Joshua E. Blumenstock, Matthew Olckers
Mobile phone-based sports betting has exploded in popularity in many African countries. Commentators worry that low-ability gamblers will not learn from experience, and may rely on…
Manipulation-Proof Machine Learning
Daniel Björkegren, Joshua E. Blumenstock, Samsun Knight
An increasing number of decisions are guided by machine learning algorithms. In many settings, from consumer credit to criminal justice, those decisions are made by applying an est…
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
Esther Rolf, Max Simchowitz, Sarah Dean +4
While real-world decisions involve many competing objectives, algorithmic decisions are often evaluated with a single objective function. In this paper, we study algorithmic polici…
Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty
Muhammad Raza Khan, Joshua E. Blumenstock
With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent app…