Fairly Estimating Socioeconomic Status Under Costly Feature Acquisition

Abstract

Predictive models have become increasingly ubiquitous in our society. However, concern has been expressed on their ability to perpetuate inequality amongst subpopulations. Active feature-value acquisition has been suggested as a method of promoting both individual and group notions of fairness in a predictive model. In this work, we seek to use such active framework to create a predictive socioeconomic model. At the same time, satellite imagery has been utilized as a method of socioeconomic estimation. In this work, our goal is to integrate satellite imagery with an active framework to create a fair predictive socioeconomic model. This was tested on one real-world dataset. Results indicate an increase in accuracy resulting from the aggregation of the satellite imagery.