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A search program that has a word embedding algorithm plugged into it can bring resumes that contain more of these related words up to the top of the search pile, hopefully helping you to find the most qualified candidates without having to read through every single document. Word embeddings do similar work in computer programs that we interact with every day -- programs that target ads at us, decide what we see on social media, or work to improve Internet search results. But here's the problem: These word embeddings learn the relationships between words by studying human writing -- like the hundreds of thousands of articles on Wikipedia or Google News. For Kalai, the problem is not that people sometimes use word embedding algorithms that differentiate between gender or race, or even algorithms that reflect human bias.