Marc Lamont Hill pushes back on Fat Joe defending his N-word use: ‘There’s no honorary degrees in Blackness’

How Tommy Robinson Disguises White Supremacism In the Coded Language of Anti-Islam Politics

Robinson repeatedly uses “native” for white, “invader” for non-white and “African” and “Somali” for black across 140 posts examined by Byline Times

Covert Racism in AI: How Language Models Are Reinforcing Outdated Stereotypes | Stanford HAI

Despite advancements in AI, new research reveals that large language models continue to perpetuate harmful racial biases, particularly against speakers of African American English.

‘This Is Nuts’: Nolan Wells’ Friends Get Judge to Silence Black Influencer from Posting About Them

Nolan Wells’ litigious friends have silenced a Black influencer, whose accusations against the teens have already put him at the center of a defamation

AI generates covertly racist decisions about people based on their dialect

Hundreds of millions of people now interact with language models, with uses ranging from help with writing1,2 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 Americans4–7. 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 movement8,9. 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.

AI generates covertly racist decisions about people based on their dialect

Hundreds of millions of people now interact with language models, with uses ranging from help with writing1,2 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 Americans4–7. 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 movement8,9. 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.

Expert: The NAACP is Fighting to Keep Black Communities From Becoming AI’s Collateral Damage – The Root

Mister Gotcha | The Nib

Calling out all hypocrisy all the way.

What do we want from Black women on screen?

From Teyana Taylor’s Oscar-nominated turn in One Battle After Another to Industry’s ruthless Harper Stern, Black women are taking on some of the most morally complex roles on screen. But what does “good” representation really mean and must it always be aspirational? Michaela Makusha investigates.

Twilight of the Racist Uncles | Ed Burmila

Younger users aren’t ditching Facebook because of Alex Jones; they are leaving because listening to all of America’s uncles at once is our literal nightmare.

Not a genuine black man : my life as an outsider : [a true story] : Copeland, Brian

xviii, 245 pages ; 23 cm

The State of the People Black Papers | Dr. David J. Johns

The State of the People Black Papers—authored by us for us, free of exaggerated jargon, written plainly in a language for our people

Bluesky is under fire for allowing usernames with racial slurs

Bluesky’s moderation woes continue as users threaten to leave the site in protest of its failure to flag slurs in account usernames. Many users — particularly Black users — are frustrated that Bluesky hasn’t apologized for allowing racial slurs to slip through its moderation tools even though they violate the platform’s community guidelines. “Our community guidelines published yesterday reflect our values for a healthy community, and we’re working on becoming better stewards every day,” Bluesky CEO Jay Graber said in a post on Saturday.