First we propose a new version of the Word Embedding Association Tests (WEATs) studied in <cite>[3]</cite> , designed to demonstrate and quantify bias in word embeddings, which puts them on a firm foundation by using the Linguistic Inquiry and Word Count (LIWC) lexica [17] to systematically detect and measure embedding biases. With this improved experimental setting, we find that European-American names are viewed more positively than African-American names, male names are more associated with work while female names are more associated with family, and that the academic disciplines of science and maths are more associated with male terms than the arts, which are more associated with female terms. Using this new methodology, we then find that there is a gender bias in the way different occupations are represented by the embedding.