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.
Supreme Court Justice Amy Coney Barrett Makes Troubling Concession to Transgender Activists During Oral Arguments
House Republican recognizes transgender Dem Sarah McBride as ‘the gentleman from Delaware’
Trump signals long road ahead in Venezuela in his boldest interventionist move yet
Bill Maher Issues Perfect Response to Fellow Celebs Wearing Anti-ICE ‘Be Good’ Pins
ICE head says agents facing ‘constant impediments’ after migrant seen ramming cars while trying to flee
Venezuela releases multiple American citizens from prison following military operation
Rand Paul says GOP colleagues ‘don’t give a s‑‑t about these people in the boats’: They ‘say they’re pro-life’
DOJ says ‘no basis’ for civil rights investigation into Minneapolis ICE officer killing
GOP senator suggests Fed chair Powell resign now to dodge potential criminal indictment
House Democrats Flip, Join Republicans to Overturn Biden-Era Regulation
Minneapolis-area leaders condemn ICE, call for removal amid Trump deportation campaign
White House says Trump gave ‘appropriate’ response after heckler confrontation caught on video at Ford plant
Suspected Venezuelan gangster in Portland CBP attack tied to shooting at apartment complex: police
Trump Issues Stern Mandate, Tells Tech Titans They Will Not Run Up American Utility Bills
RECKONING: Minnesota Republicans Preparing to Impeach Democratic Gov. Tim Walz
“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.
