College Creates AI to Identify Hate on Campus — Discover Minority Students are the Worst Abusers Jim Hoft by Jim Hoft
Radical far left students at the University of California, Berkley held a protest and formed a human chain to prevent white students from going to class in a 2016 demonstration.
A new study by Cornell Univesity using artificial intelligence found that minority students were much more likely to spread abusive racist language on twitter.
Campus Reform reported:
A new study out of Cornell reveals that the machine learning practices behind AI, which are designed to flag offensive online content, may actually “discriminate against the groups who are often the targets of the abuse we are trying to detect,” according to the study abstract.
The study involved researchers training a system to flag tweets containing “hate speech,” in much the same way that other universities are developing systems for eventual online use, by using several databases of tweets, some of which had been flagged by human evaluators for offensive content.
“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.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,” the abstract continues.
A new study out of Cornell reveals that the machine learning practices behind AI, which are designed to flag offensive online content, may actually “discriminate against the groups who are often the targets of the abuse we are trying to detect,” according to the study abstract.
The study involved researchers training a system to flag tweets containing “hate speech,” in much the same way that other universities are developing systems for eventual online use, by using several databases of tweets, some of which had been flagged by human evaluators for offensive content.
“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.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,” the abstract continues.
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