Understanding the Origins of Bias in Word Embeddings

8 Oct 2018Marc-Etienne BrunetColleen Alkalay-HoulihanAshton AndersonRichard Zemel

The power of machine learning systems not only promises great technical progress, but risks societal harm. As a recent example, researchers have shown that popular word embedding algorithms exhibit stereotypical biases, such as gender bias... (read more)

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