Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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Breiden Fehoko
Caution! Due to inherent human biases, it may seem that reports on articles aligning with our views are crafted by opponents. Conversely, reports about articles that contradict our beliefs might seem to be authored by allies. However, such perceptions are likely to be incorrect. These impressions can be caused by the fact that in both scenarios, articles are subjected to critical evaluation. This report is the product of an AI model that is significantly less biased than human analyses and has been explicitly instructed to strictly maintain 100% neutrality.
Nevertheless, HonestyMeter is in the experimental stage and is continuously improving through user feedback. If the report seems inaccurate, we encourage you to submit feedback , helping us enhance the accuracy and reliability of HonestyMeter and contributing to media transparency.
Exaggerating or sensationalizing information to attract attention.
The headline 'He would be in UFL now' is sensational and implies a dramatic fall for Jalen Hurts without substantial evidence.
Use a more neutral headline that accurately reflects the content of the article.
Avoid using hyperbolic language that exaggerates the situation.
Using language that unfairly favors one side over another.
The article uses terms like 'Dumb a**' and 'slouch' which are derogatory and biased.
Replace biased language with neutral terms.
Focus on presenting facts without using derogatory language.
Making claims without providing evidence or support.
The claim that Jalen Hurts would be in the UFL if he played for the Bengals is not supported by evidence.
Provide evidence or data to support claims.
Avoid making speculative statements without backing them up with facts.
- This is an EXPERIMENTAL DEMO version that is not intended to be used for any other purpose than to showcase the technology's potential. We are in the process of developing more sophisticated algorithms to significantly enhance the reliability and consistency of evaluations. Nevertheless, even in its current state, HonestyMeter frequently offers valuable insights that are challenging for humans to detect.