The actual scientific article is open-access: https://www.nature.com/articles/s41586-024-07856-5

Crap, I left my $199 yearly subscription info inside my butler’s Lamborghini. Could your personal valet sky-write your login credentials for nature.com above my Tuscan estate? Specifically, above the Eastern alpaca pens—this Murano glass monocle of mine isn’t a bi-focal. Cheers.

LLMs are racist... Pay us 59.99 in 3 easy payments to find out how! I love paywalled articles.

shit goes in, shit comes out

References weren't paywalled, so I assume this is the paper in question:

Hofmann, V., Kalluri, P.R., Jurafsky, D. et al. AI generates covertly racist decisions about people based on their dialect. Nature (2024).

Abstract

Hundreds of millions of people now interact with language models, with uses ranging from help with writing^1,2 to informing hiring decisions³. However, these language models are known to perpetuate systematic racial prejudices, making their judgements biased in problematic ways about groups such as African Americans^4,5,6,7. Although previous research has focused on overt racism in language models, social scientists have argued that racism with a more subtle character has developed over time, particularly in the United States after the civil rights movement^8,9. It is unknown whether this covert racism manifests in language models. Here, we demonstrate that language models embody covert racism in the form of dialect prejudice, exhibiting raciolinguistic stereotypes about speakers of African American English (AAE) that are more negative than any human stereotypes about African Americans ever experimentally recorded. By contrast, the language models’ overt stereotypes about African Americans are more positive. Dialect prejudice has the potential for harmful consequences: language models are more likely to suggest that speakers of AAE be assigned less-prestigious jobs, be convicted of crimes and be sentenced to death. Finally, we show that current practices of alleviating racial bias in language models, such as human preference alignment, exacerbate the discrepancy between covert and overt stereotypes, by superficially obscuring the racism that language models maintain on a deeper level. Our findings have far-reaching implications for the fair and safe use of language technology.

Although nonstandard English and pidgins often demonstrate the same level of nuance and complexity as standard English, it's very common for there to be negative stereotypes. One has to wonder whether the LLMs generated from (stolen en masse) written output say as much about us as they do about their creators.