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Explore the ethical implications of bias in Natural Language Processing (NLP) through this 28-minute conference talk by Navid Rekabsaz at WeAreDevelopers. Delve into the fundamentals of Word Embedding models, particularly the word2vec algorithm, and their role in capturing language semantics. Understand how these models, trained on large historical datasets, can inadvertently perpetuate inherent biases. Examine a case study revealing gender bias in occupational definitions derived from English Wikipedia text using word2vec. Gain insights into the potential impact of these biases on various NLP applications, including search engines, job recommendation platforms, and machine translation systems.
