Artificial intelligence models replicate, and sometimes amplify, all sorts of human biases. In the last few years, University of Washington researchers have found AI has erased female characters from kids’ stories, favored job applicants of certain races and genders and swayed human decisions.

Much bias research has focused on text and visuals. Yet people speak to AI models with increasing regularity, and AI tools are now used to screen job applicants and score oral presentations. Now, a UW-led team of researchers found that state-of-the-art AI models were biased against speakers with certain accents.

The team investigated how state-of-the-art multimodal language models, including two from Google and one from OpenAI, evaluated human speech in job interviews, conference presentations and spoken English proficiency tests. The models scored responses from voices speaking English with five different accents: American, British, Chinese, Indian and Nigerian.