Understanding Bias Using Machine Learning

Abstract

Socioeconomic status and gender are are heavily influenced by systemic bias. We focus on addressing systemic bias in the area of socioeconomic status by introducing an intelligent mathematical tutoring system, HWHelper. HWHelper is designed to be accessible by students, so that students who may not otherwise have help at home have access to guided instruction. The understand the systemic bias associated with gender, we introduce a framework for the identification and evaluation of gender bias in political news articles. We find that HWHelper performs well on the limited dataset. We find that our political gender bias detection system finds clear differences in the language used to describe male versus female political candidates.