Social Security chief apologizes to Congress for misleading testimony on overpayments
- Bias Rating
50% Medium Conservative
- Reliability
65% ReliableFair
- Policy Leaning
52% Medium 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
29% Positive
- Liberal
- Conservative
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
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Contributing sentiments towards policy:
62% : She also said they were "under Social Security" and "for Social Security.62% : "KFF Health News and Cox Media Group obtained a copy of the letter addressed to Rep. Drew Ferguson, R-Ga., chair of the Ways and Means' Subcommittee on Social Security, and a copy sent to a Democratic member of the committee.
55% : "Listen now and subscribe: Apple Podcasts | Google Podcasts | Spotify | RSS Feed | Omny StudioAfter the hearing, KFF Health News and Cox Media Group sent the Social Security press office several emails over a period of weeks asking for clarification: Did the numbers Kijakazi gave at the hearing represent all programs administered by the Social Security Administration, or just a subset?SSA spokesperson Nicole Tiggemann did not give a direct answer.
55% : On Dec. 18, the Senate voted 50 to 11 to confirm former Maryland Gov. Martin O'Malley as commissioner of Social Security.
52% : The head of the Social Security Administration has sent a letter of apology to members of Congress about testimony in which she understated the extent of the agency's overpayments to beneficiaries.
48% : "The hearing of the House Ways and Means Committee's Subcommittee on Social Security focused on the agency's record of sending out billions of dollars of benefit payments that it later concludes it never should have paid -- and then, sometimes years later, demanding the recipients pay the money back.
*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.