Challenges with Machine Learning Models and Biases in Text Identification

This chapter explores how machine learning models struggle with understanding slang and non-anglicized English text, leading to a disproportionate flagging of posts from students of color. It discusses a study where African-American vernacular English was mistaken for Dutch by one of these models, highlighting biases rooted in training data and raising questions about the use of this software in schools.