Border Patrol agents who posted racist, sexist content in Facebook groups remain on the job
- Bias Rating
-6% Center
- Reliability
N/AN/A
- Policy Leaning
-58% Medium Liberal
- Politician Portrayal
-5% Negative
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The A.I. bias rating includes policy and politician portrayal leanings based on the author’s tone found in the article using machine learning. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral.
Sentiments
N/A
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- Conservative
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
Bias Meter
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-100%
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Contributing sentiments towards policy:
58% : The report made several recommendations for CBP to hold employees accountable on social media.52% : Such records showed that CBP knew about the agents' inappropriate Facebook posts three years before they were publicized in news outlets.
50% : Rep. Carolyn B. Maloney, D-N.Y., the committee chair, said the report's findings represent the "broken disciplinary process" at CBP.
47% : I'm 10-15" - a code used by Border Patrol to refer to migrants in custody.
44% : CBP did not immediately respond to a request for comment.
40% : The majority of U.S. Customs and Border Protection agents who committed misconduct in private Facebook groups that featured racist and sexist posts against migrants and members of Congress received inadequate penalties and remain on the job, according to a congressional investigation.
40% : "These outcomes were the result of a number of failings at CBP, including an inconsistent disciplinary process, a failure to train on and enforce social media policies, and senior leadership's failure to take appropriate actions despite knowledge of these Facebook groups," the report said, which was prepared by staff from the House committee's Democratic majority.
35% : "As we saw with the mistreatment of migrants by Border Patrol agents in Del Rio, Texas last month, systemic behavior problems within CBP persist."
*Our bias meter rating uses data science including sentiment analysis, machine learning and our proprietary algorithm for determining biases in news articles. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral. The rating is an independent analysis and is not affiliated nor sponsored by the news source or any other organization.