# De-Biasing Word Embeddings Word embeddings can reflect the gender, ethnicity, age, sexual orientation and other biases of the text used to train the model. For example, Man->Doctor, Woman->Nurse, which is not a correct mapping, and is biased based on gender. The following is an overview of the steps to address this bias: 1. Identify the bias direction 2. Neutralize i.e. for every word that is not definitional (i.e. isn't defined to satisfy a given bias, for example, 'father' is defitional as it is only defined for the male gender, but 'doctor' is not definitional since it isn't defined for a particular gender), project it in the non-bias direction to get rid of bias 3. Equalize the definitional words, i.e. make them equidistant from the non defitional words --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://vikram-bajaj.gitbook.io/deep-learning-specialization-coursera/main-10/sequence-models/natural-language-processing-and-word-embeddings/de-biasing-word-embeddings.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.