Why creating an international body for AI is a bad idea
- Bias Rating
74% Very Conservative
- Reliability
45% ReliableFair
- Policy Leaning
-10% Center
- Politician Portrayal
N/A
Continue For Free
Create your free account to see the in-depth bias analytics and more.
Continue
Continue
By creating an account, you agree to our Terms and Privacy Policy, and subscribe to email updates. Already a member: Log inBias Score Analysis
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
19% Positive
- Liberal
- Conservative
Sentence | Sentiment | Bias |
---|---|---|
Unlock this feature by upgrading to the Pro plan. |
Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
Bias Meter
Extremely
Liberal
Very
Liberal
Moderately
Liberal
Somewhat Liberal
Center
Somewhat Conservative
Moderately
Conservative
Very
Conservative
Extremely
Conservative
-100%
Liberal
100%
Conservative
Contributing sentiments towards policy:
59% : The EU fined Amazon $888 million in 2021, Alphabet's Google $2.7 billion in 2017, another $5.1 billion in 2018 and Meta a cool $1.3 billion last May.58% : This is not because they don't have the proper information, but because "the knowledge problem" is an inherent weakness of regulation.
54% : Market forces are much better at handling that kind of promising dynamism than state planners will ever be.
53% : The IPCC was established by the World Meteorological Organisation (WMO) and the United Nations Environmental Programme (UNEP) in 1988 with the same lofty goals for climate change policy advising that Schmidt invokes for AI.
48% : It's hard to imagine the EU or China acting impartially toward U.S. AI companies if given seats at the global advising table.
*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.