Bitcoin Billionaire Saylor Will Settle D.C. Tax Suit For $40 Million
- Bias Rating
10% Center
- Reliability
70% ReliableGood
- Policy Leaning
12% Somewhat Conservative
- Politician Portrayal
2% 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
-13% Negative
- Liberal
- 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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100%
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Contributing sentiments towards policy:
59% : "The lawsuit, the first of its kind under the amended D.C. False Claims Act (FCA), alleged that for decades Saylor falsely claimed to live in lower-tax Virginia and Florida to avoid paying the income taxes he would owe to the District of Columbia as a city resident.49% : For income tax reasons, residency is your physical location and domicile is where you intend to remain.
48% : Most states will consider you a resident for income tax purposes if you spend 183 days or more in that state.
45% : As with federal tax whistleblowing statutes, there are limits -- for tax-related claims to be successful, the District taxable income, District sales, or District revenue related to the action must be $1 million or more for any taxable year, and the harm to the District must be $350,000 or more.
41% : The law was expanded to include specific references to taxation and increase the reward for informants who report tax fraud -- that might have been the impetus for the Saylor filing.
40% : The OAG released a statement from Schwalb, saying, in part, "Tax cheats are freeloading off the backs of hardworking, law-abiding, tax paying District residents while depriving our city of resources needed for critical programs, including public safety, infrastructure, and education.
*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.