Poster Presentation
Workshop: 2nd Workshop on Practical ML for Developing Countries: Learning Under Limited/low Resource Scenarios

Fairly Estimating Socioeconomic Status Under Costly Feature Acquisition

Kush R Varshney


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. 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.