Wes Moore calls out Laura Loomer for ‘peddling racism’ in attack on Black women

Laura Loomer’s racist attacks on Angie Nixon and other prominent Black women draw backlash

Nixon fired back at the far-right activist as Marjorie Taylor Greene publicly condemned Loomer’s remarks as “nasty” and “hateful.”

Senator Scott Wiener (@scottwiener.bsky.social)

The normalization of anti-Muslim rhetoric is reaching new levels. Yes, Loomer is nuts, but her extremist views at times become mainstream GOP thinking. Muslims are part of our nation’s fabric, just as Christians & Jews are. We’re all American. This bigotry needs to stop. https://www.mediamatters.org/laura-loomer/laura-loomer-pitches-banning-muslims-running-office-members-congress

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

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.

Data Feminism for AI

Attacks on women and trans people are not happening in a vacuum. Communications scholars W. Lance Bennett and Marianne Kneuer recently argued that we are witnessing the intensification of "illiberal public spheres" around the world (Bennett and Kneuer, 2023). These are characterized by a set of anti-democratic communications tactics that include the denigration and exclusion of minoritized people, targeted attacks on the press and political institutions, and transgression of norms of civil discourse. Such tactics are enabled and amplified by the social media platforms owned by Big Tech, whose business model elevates attention above all other metrics, such that they profit richly off extremism, threats, spectacle, and lies. The success of this corporate partnership with right-wing extremism is evident in the proliferation of book bans (Magnusson, 2024), the demonization of DEI and critical race theory (Crenshaw, 2022), and mob tactics to doxx, intimidate, misrepresent, and otherwise silence people that study misinformation, speak out against racial and gender violence, or rule against insurrectionists (Edmonds, 2024). At the same time, we have seen the continued work of feminists, including Latin American, Indigenous, and abolitionist feminists in the US and globally, doing the work of imagining alternate worlds (Cullors, 2021; Davis, 2022; Ricaurte, 2019; Simpson, 2017). This paper represents our own first attempt to imagine a more equitable, sustainable, feminist world for AI research.

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.

The Anti-Defamation League and the Racial State by Emmaia Gelman - Hardcover

Scholarship is a powerful tool for changing how people think, plan, and govern. By giving voice to bright minds and bold ideas, we seek to foster understanding and drive progressive change.

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

How Anti-LGBTQ+ Rhetoric Fuels Violence

Hate speech from the far right is increasing the risk of violence against LGBTQ+ people such as the Club Q shooting

El-Sayed responds after Vance calls him a "weasel" as Michigan Senate race grows contentious

Abdul El-Sayed told Michigan voters the state's U.S. Senate race could go down "as one of the ugliest campaigns in modern history."

Michigan Senate debate: Fact-checks of El-Sayed, Rogers

Abdul El-Sayed, the Democratic nominee for Senate in Michigan, and his Republican opponent Mike Rogers, a former U.S. House representative, met for the first time in an Oct. 8 debate, with each candidate trying to drag down the other through a series of personal attacks that have become the trademark of their nasty campaign. In […]

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.

Intersectional Antifeminism, or: What is a White Feminist, Anyway?

Regarding the most famous social theory no one has actually read.

Between Threat and Reality: The National Association for the Advancement of Colored People and the Emergence of Armed Self-Defense in Clarksdale and Natchez, Mississippi, 1960-1965

Annelieke Dirks, Between Threat and Reality: The National Association for the Advancement of Colored People and the Emergence of Armed Self-Defense in Clarksdale and Natchez, Mississippi, 1960-1965, Journal for the Study of Radicalism, Vol. 1, No. 1 (Spring 2007), pp. 71-98