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.
Colorado school staffer accused of sexually abusing 13-year-old student, now on the run
State Department to ask for bonds of up to $15,000 for visa applications from a dozen more countries
Former counterterrorism chief Joe Kent under FBI investigation for alleged classified leaks
‘Theatrical fools’: House GOP slams Democrats for blowing off Bondi interview
186 Democrats Kick and Scream as Critical Bill Targeting Fraudsters Narrowly Passes House
BREAKING: Sen. Kennedy Just Moved to Pass the SAVE America Act Using a Brilliant Legislative Trick That Only Requires 51 Votes
Immigration judge orders deportation of NYC Council employee after ICE arrest, city leaders push back
Millionaire developer found dead in ritzy beach home; bizarrely dressed suspect arrested after standoff
GOP overperforms in Virginia special election, fueling early momentum talk in blue-trending state
Senate Republicans side with Trump in second Iran war vote
As Dems Shill for Iran Against Trump, Reports Claim Iranian Security Is Raping Women So Badly That They Need Colostomy Bags and Hysterectomies
Watch: Senate Dem Turns to Conspiracy Theories When Markwayne Mullin Refuses to Play Her Games During Confirmation Hearing
MS Now Axes Part of ‘Morning Joe’ in Significant Shake-Up
Mullin committee vote could hinge on John Fetterman as Rand Paul vows to oppose
How Markwayne Mullin would lead DHS differently than Kristi Noem
“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.
