Explore gender stereotypes in word embeddings and learn techniques to quantify and reduce bias in this hands-on tutorial from the FAT* 2018 conference. Dive into the basics of word embedding learning and applications, then gain practical experience writing programs to display and measure gender stereotypes in these widely-used natural language processing tools. Discover methods to mitigate bias and create fairer algorithmic decision-making processes. Work with iPython notebooks to explore real-world examples and complete exercises that reinforce concepts of fairness in machine learning and natural language processing.