Algorithmic Inclusion: A Scalable Approach to Reducing Gender Bias in Google Translate | AISC

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=JD-h1HHYXRM


Duration: 59:43
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For slides and more information on the paper, visit https://ai.science/e/algorithmic-inclusion-a-scalable-approach-to-reducing-gender-bias-in-google-translate--ASl68X3Gfz4pQ94QaD4U

Speaker: Melvin Johnson; Discussion Facilitator: Serena McDonnell

Motivation:
Machine learning (ML) models for language translation can be skewed by societal biases reflected in their training data. One such example, gender bias, often becomes more apparent when translating between a gender-specific language and one that is less-so. For instance, Google Translate historically translated the Turkish equivalent of “He/she is a doctor” into the masculine form, and the Turkish equivalent of “He/she is a nurse” into the feminine form.



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