How machine-learning code turns a mirror on its sexist, racist masters

Be careful which words you feed into that machine-learning software you're building, and how. A study of news articles and books written during the 20th and 21st century has shown that not only are gender and ethnic stereotypes woven into our language, but that algorithms commonly used to train code can end up unexpectedly baking these biases into AI models. Basically, no one wants to see tomorrow's software picking up yesterday's racism and sexism. A paper published in the Proceedings of the US National Academy of Sciences on Tuesday describes how word embeddings, a common set of techniques used by machine-leaning applications to develop associations between words, can pick up social attitudes towards men and women, and people of different ethnicities, from old articles and novels. In word-embedding models, an algorithm converts each word into a mathematical vector and maps it to a latent space.