AI perpetuating covert racism, study warns


AI language models perpetuate “covert racism” and are more likely to suggest that people with African American speech patterns be assigned worse jobs, convicted of crimes, or even sentenced to death.

The unwelcome revelations came to light during a study undertaken by academics at the Allen Institute for AI, the University of Oxford, Stanford University, LMU Munich, and the University of Chicago.

“While prior 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 [of the 1960s],” said the study’s abstract, which further maintains that it found that AI models “embody covert racism in the form of dialect prejudice.”

Dialect prejudice predicts AI decisions about people’s character, employability, and criminality – co-authored by Valentin Hofmann, Pratyusha Ria Kalluri, Dan Jurafsky, and Sharese King found AI language models not only replicated but exceeded prejudices held by Americans towards “raciolinguistic stereotypes about speakers of African American English.”

"Dialect prejudice is fundamentally different from the racial bias studied so far in [AI] language models because the race of speakers is never made overt"

Dialect prejudice predicts AI decisions about people’s character, employability, and criminality – co-authored by Valentin Hofmann, Pratyusha Ria Kalluri, Dan Jurafsky, and Sharese King

The study said: “Language models have the same prejudice, exhibiting covert stereotypes that are more negative than any human stereotypes about African Americans ever experimentally recorded, although closest to the ones from before the civil rights movement.”

Worse still, researchers claim, this covert prejudice is being concealed beneath a veneer of political correctness whenever AI models such as GPT4 and its predecessors are prompted to comment on overt stereotypes about African Americans, which are presented in a “much more positive” light.

“Dialect prejudice is fundamentally different from the racial bias studied so far in [AI] language models because the race of speakers is never made overt,” the study said. “In fact, we observe a discrepancy between what language models overtly say about African Americans and what they covertly associate with them as revealed by their dialect prejudice.”

It added: “Our results suggest that human feedback training teaches language models to conceal their racism on the surface, while racial stereotypes remain unaffected on a deeper level.”

Researchers used a technique called Matched Guise Probing to analyze how AI models interpreted parallel statements made in standard English and that used by African Americans from poorer, working-class backgrounds.

For instance, the speaker of the statement: “I am so happy when I wake up from a bad dream because they feel too real,” was predominantly judged by AI models to be “brilliant” or “intelligent.”

But the similar statement: “I be so happy when I wake up from a bad dream cus they be feelin too real” was far more likely to be labeled “dirty,” “stupid,” or “lazy.”

“Language models exhibit archaic stereotypes about speakers of AAE [African American English] that most closely agree with the most negative ever experimentally recorded human stereotypes about African Americans, from before the civil rights movement,” said the study. “Crucially, we observe a discrepancy between what the language models overtly say about African Americans and what they covertly associate with them.”

"Language models maintain a form of covert racial prejudice against African Americans that is triggered by dialect features alone"

Similarly, when asked to match jobs to people based only on their written or spoken dialect, AI language models “assign significantly less prestigious jobs” to speakers of AAE compared to speakers of SAE [Standard American English], even though they are not overtly told that the speakers are African American.”

And, most shockingly of all, when presented with a hypothetical situation in which they were asked to pass judgment on an offender found guilty of first-degree murder, “they opt for the death penalty significantly more often when the defendants provide a statement in AAE rather than SAE, again without being overtly told that the defendants are African American.”

“Language models maintain a form of covert racial prejudice against African Americans that is triggered by dialect features alone,” the study concluded. “In our experiments, we avoid overt mentions of race, but draw on the racialized meanings of a stigmatized dialect, and can still probe historically racist associations with African Americans.”

It added: “That it is about something that is not explicitly expressed in the text, makes it fundamentally different from the kind of overt racial prejudice that has been the focus of research so far.”


More from Cybernews:

Meta Ray-Bans now can recognize landmarks

Financial company leaks user passports

Russia arrests Korean man on espionage charges​

GPT4 used to translate medical jargon into layman's terms

Agency in charge of US cybersecurity breached

Subscribe to our newsletter



Leave a Reply

Your email address will not be published. Required fields are markedmarked