5 citations · 9 across the 10 of their papers we have counts for
10 papers
Predicting Well-Being with Mobile Phone Data: Evidence from Four Countries
M. Merritt Smith, Emily Aiken, Joshua E. Blumenstock +1
We provide systematic evidence on the potential for estimating household well-being from mobile phone data. Using data from four countries - Afghanistan, Cote d'Ivoire, Malawi, and…
On the Meta-Design of Allocation Problems
Unai Fischer-Abaigar, Emily Aiken, Christoph Kern +1
There is an extensive literature that studies how to find optimal policies in resource allocation problems, taking the underlying design parameters that define the allocation, such…
A Machine Learning Approach for Detection of Mental Health Conditions and Cyberbullying from Social Media
Edward Ajayi, Martha Kachweka, Mawuli Deku +1
Mental health challenges and cyberbullying are increasingly prevalent in digital spaces, necessitating scalable and interpretable detection systems. This paper introduces a unified…
Expanding Perspectives on Data Privacy: Insights from Rural Togo
Zoe Kahn, Meyebinesso Farida Carelle Pere, Emily Aiken +2
Passively collected "big" data sources are increasingly used to inform critical development policy decisions in low- and middle-income countries. While prior work highlights how su…
Privacy Guarantees for Personal Mobility Data in Humanitarian Response
Nitin Kohli, Emily Aiken, Joshua Blumenstock
Personal mobility data from mobile phones and other sensors are increasingly used to inform policymaking during pandemics, natural disasters, and other humanitarian crises. However…
Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy
Emily Aiken, Esther Rolf, Joshua Blumenstock
Poverty maps derived from satellite imagery are increasingly used to inform high-stakes policy decisions, such as the allocation of humanitarian aid and the distribution of governm…
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…
Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan
Emily Aiken, Guadalupe Bedoya, Joshua Blumenstock +1
Can mobile phone data improve program targeting? By combining rich survey data from a "big push" anti-poverty program in Afghanistan with detailed mobile phone logs from program be…
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…
Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions
Emily L. Aiken, Andre T. Nguyen, Mauricio Santillana
We introduce the use of a Gated Recurrent Unit (GRU) for influenza prediction at the state- and city-level in the US, and experiment with the inclusion of real-time flu-related Int…