Researchers from the University of Cornell discovered that artificial intelligence systems designed to identify offensive “hate speech” flag comments purportedly made by minorities “at substantially higher rates” than remarks made by whites.
Several universities maintain artificial intelligence systems designed to monitor social media websites and report users who post “hate speech.” In a study published in May, researchers at Cornell discovered that systems “flag” tweets that likely come from black social media users more often, according to Campus Reform.
The study’s authors found that, according to the AI systems’ definition of abusive speech, “tweets written in African-American English are abusive at substantially higher rates.”
The study also revealed that “black-aligned tweets” are “sexist at almost twice the rate of white-aligned tweets.”
The research team averred that the unexpected findings could be explained by “systematic racial bias” displayed by the human beings who assisted in spotting offensive content.
Trump admin’s fraud crackdown targets foreigners accused of infiltrating U.S. federal programs
Giuliani blasts Mamdani’s 9/11 toxic air blame as buried city records come to light
In pictures: Trump, Bush, and more pay their respects at 9/11 ceremonies across three sites on 25th anniversary
WATCH LIVE: Trump, first lady, Hegseth, and more attend 9/11 memorial at the Pentagon
Sept. 11 in photos: Washington Examiner photographer Graeme Jennings shares his images from the aftermath of 9/11
The Ghouls Come Out After Erika Kirk Breaks Her Silence on Anniversary of Charlie’s Death
We Forgot 9/11: Then We Swore a Mayor In on the Qur’an – Part Two
25 years later, 9/11 memories endure as nation gathers to honor nearly 3,000 lives lost
Ontario premier wants Republicans to lose House and Senate over Trump’s trade war
The unlucky 13: States bordering Canada are particularly hard-hit by trade war
Wendell Berry, 1934-2026
Trump admin’s list of US-designated ‘foreign terrorist’ groups in Latin America expands to 21
‘I’m coming home’: Last words haunt a 9/11 daughter 25 years later as she builds a legacy in his honor
Hasan Piker, cowboys, Charlie Kirk, and more: Top six takeaways from Republican midterm convention Night 2
Vance describes his hopes for baby son as he lays down a marker for 2028 with the speech of his life
“The results show evidence of systematic racial bias in all datasets, as classifiers trained on them tend to predict that tweets written in African-American English are abusive at substantially higher rates,” reads the study’s abstract. “If these abusive language detection systems are used in the field they will, therefore, have a disproportionate negative impact on African-American social media users.”
One of the study’s authors said that “internal biases” may be to blame for why “we may see language written in what linguists consider African American English and be more likely to think that it’s something that is offensive.”
Automated technology for identifying hate speech is not new, nor are universities the only parties developing it. Two years ago, Google unveiled its own system called “Perspective,” designed to rate phrases and sentences based on how “toxic” they might be.
Shortly after the release of Perspective, YouTube user Tormental made a video of the program at work, alleging inconsistencies in implementation.
According to Tormental, the system rated prejudicial comments against minorities as more “toxic” than equivalent statements against white people.
Google’s system showed a similar discrepancy for bigoted comments directed at women versus men.
Story cited here.
