On the limits of algorithmic prediction across the globe

by   Xingyu Li, et al.

The impact of predictive algorithms on people's lives and livelihoods has been noted in medicine, criminal justice, finance, hiring and admissions. Most of these algorithms are developed using data and human capital from highly developed nations. We tested how well predictive models of human behavior trained in a developed country generalize to people in less developed countries by modeling global variation in 200 predictors of academic achievement on nationally representative student data for 65 countries. Here we show that state-of-the-art machine learning models trained on data from the United States can predict achievement with high accuracy and generalize to other developed countries with comparable accuracy. However, accuracy drops linearly with national development due to global variation in the importance of different achievement predictors, providing a useful heuristic for policymakers. Training the same model on national data yields high accuracy in every country, which highlights the value of local data collection.


page 1

page 2

page 3

page 4


High-Resolution Poverty Maps in Sub-Saharan Africa

Up-to-date poverty maps are an important tool for policy makers, but unt...

Explainable Machine Learning for Predicting Homicide Clearance in the United States

Purpose: To explore the potential of Explainable Machine Learning in the...

Estimation of World Seroprevalence of SARS-CoV-2 antibodies

In this paper, we estimate the seroprevalence against COVID-19 by countr...

Modeling PKT at a global level: A machine learning approach

It is well-accepted that the ability to go from one place to another, or...

Household poverty classification in data-scarce environments: a machine learning approach

We describe a method to identify poor households in data-scarce countrie...

Accuracy Convergent Field Predictors

Several predictive algorithms are described. Highlighted are variants th...