Sponsored by Women at the Table
Full paper found here.
Full analysis including explanations and source code here.
Most people require access to emergency funds at least once in their life. These funds act as an important safety net in emergency cases. The purpose of our project is to predict access to emergency funds for adults in Sub-Saharan countries using a human rights-based approach to machine learning which centers equity, fairness, and impacts on humans over accuracy. Our analysis is based on the 2017 Global Findex Database which includes demographic as well as financial information for a sample of individuals within each country. We used a Decision Tree Classifier machine learning model implemented using Python to predict access to emergency funds with 68% accuracy. We assessed the fairness of our model with respect to gender using a variety of group and individual fairness metrics and evaluated the implications of each fairness metric with regard to our data and the goals of the analysis. We then implemented a variety of pre-proccessing, in-processing, and post-processing techniques in an attempt to minimize bias and maximize fairness. We have documented our analysis in a Jupyter notebook where this information can be made accessible to a broader undergraduate audience.