By - Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, Adam Kalai Boston University, 8 Saint Mary’s Street, Boston, MA Microsoft Research New England, 1 Memorial Drive, Cambridge, MA Paper Link Todays topic is part of Explainable ai ( previous blog post ). DeBiasing is one of the aspect that influence Model decision. Abstract The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases. Geometrically, gender bias is first shown to be captured by a direction in the word embedding. Second, gender neutral words are...