Can new government regulation re-pack the Pandora's Box of emerging AI tools?
- Bias Rating
8% Center
- Reliability
75% ReliableGood
- Policy Leaning
10% Center
- Politician Portrayal
34% Positive
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
N/A
- 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:
54% :Several ideas were raised by lawmakers about how new government oversight efforts could be constructed, including extending responsibilities to existing agencies, launching an AI-dedicated regulatory agency in the vein of the FCC or FTC, or even creating a new cabinet-level position to oversee AI-related issues.50% : Marcus noted that a widely agreed upon AI "constitution" could form a starting point for how to proceed on regulation and could apply to either a domestic or international body tasked with AI oversight.
40% : Montgomery advocated for a "precision" regulatory approach like the one currently under consideration by the European Union, the AI Act, that would create specific regulatory boundaries based on four risk levels that include unacceptable risk, high risk, limited risk, and minimal or no risk.
*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.