The Souls of White Jokes | Stanford University Press

Bluesky civil war shows free speech is harder than it looks

Last week, a joke familiar from X circulated on rival platform Bluesky: “(bluesky user bursts into Waffle House) OH SO YOU HATE PANCAKES??” It was obviously a jab at the moral intensity that now seems to define the site, and indeed much of the rest of the social media landscape. On most platforms such a [...]Read More...

Musk’s AI Grok bot rants about ‘white genocide’ in South Africa in unrelated chats

X chatbot tells users it was ‘instructed by my creators’ to accept ‘white genocide as real and racially motivated’

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.

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

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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.

Beyond Afrofuturism: Sinners, the Great Migration, and Rust Belt Gothik - Reactor

"Sinners" is a particular kind of story about migration, spirituality, and Black speculative survival skills

Analytic racecraft: Race-based averages create illusory group differences in perceptions of racism.

Digital blackface

Digital blackface is a term used to describe the phenomenon of non-Black individuals using digital media, such as GIFs, memes, or audio clips featuring Black individuals, to express emotions or convey ideas. This behavior has sparked debate and criticism due to concerns about cultural appropriation and the perpetuation of stereotypes. Digital blackface has been described as "one of the most insidious forms of contemporary racism"[1] and has been compared to historical minstrelsy by Black individuals and social justice advocates.

Footnotes by Jemar Tisby | Substack

Truth-telling at the intersection of faith, history, and justice Focus on white Christian nationalism + the Black Christian tradition. Click to read Footnotes by Jemar Tisby, by Jemar Tisby, PhD, a Substack publication with tens of thousands of subscribers.

Commentary: Cory Doctorow: Hell Is Other People - Locus

So this is hell. I'd never have believed it. You remember all we were told about the torture-chambers, the fire and brimstone, the 'burning marl.' Old wives' tales! There's no need for red-hot pokers. HELL IS – OTHER PEOPLE! –Jean Paul Sartre, No Exit Sartre was (arguably) an optimist. It's not that other people are unpleasant: quite the contrary! There's nothing that makes the day (or, pointedly, the night) …Read More