Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
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 shown to be linearly separable from gender definition words in the word embedding. Using these properties, we provide a methodology for modifying an embedding to remove gender stereotypes, such as the association between the words receptionist and female, while maintaining desired associations such as between the words queen and female. Using crowd-worker evaluation as well as standard benchmarks, we empirically demonstrate that our algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks. The resulting embeddings can be used in applications without amplifying gender bias.
Word embedding approach is wildly used in many machine learning tasks. Because it is learned from huge collected language dataset like Google News articles, the serious stereotypes may be inherent in the dataset and they typically result in the bias model. The authors initiate the study of stereotypes in word embeddings and try to develop two metrics to quantify gender stereotypes through crowdsourcing. In addition, the authors focus on gender stereotypes and propose an algorithm for learning a transformation to remove it from the embedding while preserving useful properties in a low-dimensional subspace. A experiment is shown to demonstrate that the proposed approach addresses the gender stereotypes issue and does not negatively impact the performance on the standard evaluation metrics measuring coherence and analogy-solving abilities.
The paper addresses a novel problem which is inherent in the wildly used word embedding model. However, there are indeed something can be improved.           -) Although the idea is quite novel, the technical contributions in this work are limited. I wonder that the proposed debiasing algorithm useing PCA to identify the stereotype subspace and a linear transformation to reduce it could work on more complex stereotype problems.           -) It lacks the systematic analysis to demonstrate the usefulness of the proposed debiasing algorithm. In other words, it lacks the higher level natural language experiment to prove the effectiveness of the proposed method.           -) The analysis for the several hyperparameters such like δ and the threshold of the score S at line 146 and line 151 are missed.           -) The number of analogies for experiments are too small. It may increase the number of analogies from other stereotypes (e.g. race, cultural, employment).           -) There are several typos in the article             line 10 between between -> between             line 31 [17] Consider -> [17]. Consider             line 33 networks.” -> networks.”.             line 163 were judged to -> were judged to.             line 231 a a -> a             line 246 corresponds corresponds -> corresponds             line 278 Table -> Figure2.             The article needs to be proofread before submission.           -) The readability of the section "Step 3: surgery to modify gender-definition words" is too terrible to follow. It needs to be revised and clarified.           I would request the authors to provide response to the questions/explainations as mentioned above.