Federal technology staffers resign rather than help Musk and DOGE
- Bias Rating
10% Center
- Reliability
65% ReliableAverage
- Policy Leaning
10% Center
- Politician Portrayal
-47% 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.
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Contributing sentiments towards policy:
59% : U.S. Digital Service veterans, who spoke on the condition of anonymity out of fear of reprisal, recalled experiencing a similar sort of shock about how government processes worked that Musk and his team are discovering.47% : Many were young and seemed guided by ideology and fandom of Musk — not improving government technology.
42% : “These highly skilled civil servants were working to modernize Social Security, veterans’ services, tax filing, health care, disaster relief, student aid, and other critical services,” the resignation letter states.
40% : ”The slash-and-burn effort Musk is leading diverges from what was initially outlined by Trump during the 2024 presidential campaign.
36% : The day after Trump’s inauguration, the staffers wrote, they were called into a series of interviews that foreshadowed the secretive and disruptive work of Musk’s’ Department of Government Efficiency, or DOGE.
35% : “Anyone who thinks protests, lawsuits, and lawfare will deter President Trump must have been sleeping under a rock for the past several years,” Leavitt said.
31% : WASHINGTON (AP) — More than 20 civil service employees resigned Tuesday from billionaire Trump adviser Elon Musk’s Department of Government Efficiency, saying they were refusing to use their technical expertise to “dismantle critical public services.
30% : “President Trump will not be deterred from delivering on the promises he made to make our federal government more efficient and more accountable to the hardworking American taxpayers.”
*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.