Media Manipulation and Bias Detection
Auto-Improving with AI and User Feedback
- Objectivity Score
- Objectivity Score
- Objectivity Score
- Objectivity Score
- Objectivity Score
- Objectivity Score
Media Manipulation and Bias Detection
Auto-Improving with AI and User Feedback
HonestyMeter - AI powered bias detection
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Jon Rahm
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 aspects of the story to attract attention.
The headline 'Entire crowd wanted Phil Mickelson to win' is sensationalized, as it implies unanimous support without evidence.
Use a more measured headline such as 'Phil Mickelson received significant crowd support against Jon Rahm.'
Using language that unfairly favors one side over another.
The phrase 'Rahm’s Legion XIII dominated against Phil Mickelson’s HyFlyers GC' suggests a one-sided victory without providing detailed context.
Provide specific details about the match to support the claim of domination or use more neutral language like 'Rahm’s Legion XIII won against Phil Mickelson’s HyFlyers GC.'
- 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.