Bias and stereotyping: Human and artificial intelligence (AI)

Abstract As social and educational landscapes continue to change, especially around issues of inclusivity, there is an urgent need to reexamine how individuals from diverse linguistic backgrounds are perceived. Speakers are often misjudged due to listeners’ stereotypes about their social identities, resulting in biased language judgments that can limit educational and professional opportunities. Much research has demonstrated listeners’ biases toward L2-accented speech, i.e., perceiving accented utterances as less credible, less grammatical, or less acceptable for certain professional positions, due to their bias and stereotyping issues. Then, artificial intelligence (AI) technology has emerged as a viable alternative to mitigate listeners’ biased judgments. It serves as a tool for assessing L2-accented speech as well as establishing intelligibility thresholds for accented speech. It is also used to assess characteristics such as gender, age, and mood in AI facial-analysis systems. However, these AI systems or current technologies still may hold racial or accent biases. Accordingly, the current paper will discuss both human listeners’ and AI’ bias issues toward L2 speech, illustrating such phenomena in various contexts. It concludes with specific recommendations and future directions for research and pedagogical practices.