The UW researchers tested three open-source, large language models (LLMs) and found they favored resumes from white-associated names 85% of the time, and female-associated names 11% of the time. Over the 3 million job, race and gender combinations tested, Black men fared the worst with the models preferring other candidates nearly 100% of the time.
Why do machines have such an outsized bias for picking white male job candidates? The answer is a digital take on the old adage “you are what you eat.”
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