Treasury Official Quits After Resisting Musk's Requests on Payments
- Bias Rating
10% Center
- Reliability
70% ReliableGood
- Policy Leaning
10% Center
- Politician Portrayal
-22% 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
20% Positive
- Liberal
- Conservative
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
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Contributing sentiments towards policy:
55% : The system is run out of the Bureau of the Fiscal Service, a little-known but critical office that is responsible for getting money to Social Security recipients, government employees, contractors and others.51% : The decision by Mr. Musk's efficiency team to integrate into the federal government, rather than set up an outside body, has been driven by its view that burrowing into the existing U.S. Digital Service will give it greater visibility into federal spending.
49% : The Trump administration pushed out a top Treasury Department official this week after he refused to give Elon Musk's cost-cutting team access to the government's vast payment system, part of a bid by the so-called Department of Government Efficiency to choke off federal funding.
45% : "The American people should not have to worry about political interference when it comes to receiving Social Security and other payments the fiscal service makes.
44% : " Mr. Akis has made similar inquiries at the I.R.S. about its information technology as part of an effort to automate tax collection, according to people familiar with the matter.
43% : The White House and a representative of Mr. Musk's Department of Government Efficiency did not return requests for comment.
40% : Mr. Musk has been fixated on the Treasury system as a key to cutting federal spending.
*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.