[D] Machine Learning - WAYR (What Are You Reading) - Week 94

TL;DR This paper uses a technique called "iterative nullspace projection (INLP)" in order to debias word embedding representations. They do this by training linear classifiers to predict a certain attribute that they don't want the word embeddings to contain (e.g., race or gender information) and then project the weights of this classifier into the nullspace of the weight matrix. They perform this process iteratively in order to increasingly remove bias components. I personally found the paper really interesting because they took a bit of a different approach to typical NLP papers. One thing that I do find that needs work, however, is that their method does not fully address the "bias by neighbors" issue.