What we learn from AI's biases

In "How to Make a Racist AI Without Really Trying," Robyn Speer shows how to build a simple sentiment analysis system, using standard, well-known sources for word embeddings (GloVe and word2vec), and a widely used sentiment lexicon. Her program assigns "negative" sentiment to names and phrases associated with minorities, and "positive" sentiment to names and phrases associated with Europeans. Even a sentence like "Let's go get Mexican food" gets a much lower sentiment score than "Let's go get Italian food." That result isn't surprising, nor are Speer's conclusions: if you take a simplistic approach to sentiment analysis, you shouldn't be surprised when you get a program that embodies racist, discriminatory values. It's possible to minimize algorithmic racism (though possibly not eliminate it entirely), and Speer discusses several strategies for doing so.