Researchers claim bias in AI named entity recognition models

Twitter researchers claim to have found evidence of demographic bias in named entity recognition, the first step toward generating automated knowledge bases, or the repositories leveraged by services like search engines. They say their analysis reveals AI performs better at identifying names from specific groups and the biases manifest in syntax, semantics, and how word uses vary across linguistic contexts. Knowledge bases are essentially databases containing information about entities -- people, places, and things. In 2012, Google launched a knowledge base -- the Knowledge Graph -- to enhance Google search results with hundreds of billions of facts gathered from sources including Wikipedia, Wikidata, and CIA World Factbook. Microsoft provides a knowledge base with over 150,000 articles created by support professionals who've resolved issues for its customers. But while the usefulness of knowledge bases is not in dispute, the researchers assert the embeddings used to represent entities in them exhibit bias against certain groups of people.