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Enlarge (credit: Aurich Lawson | Getty Images)
As far back as 2016, work on AI-based chatbots revealed that they have a disturbing tendency to reflect some of the worst biases of the society that trained them. But as large language models have become ever larger and subjected to more sophisticated training, a lot of that problematic behavior has been ironed out. For example, I asked the current iteration of ChatGPT for five words it associated with African Americans, and it responded with things like "resilience" and "creativity."
But a lot of research has turned up examples where implicit biases can persist in people long after outward behavior has changed. So some researchers decided to test whether the same might be true of LLMs. And was it ever.
By interacting with a series of LLMs using examples of the African American English sociolect, they found that the AI's had an extremely negative view of its speakers—something that wasn't true of speakers of another American English variant. And that bias bled over into decisions the LLMs were asked to make about those who use African American English.
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