Media Manipulation and Bias Detection
Auto-Improving with AI and User Feedback
HonestyMeter - AI powered bias detection
CLICK ANY SECTION TO GIVE FEEDBACK, IMPROVE THE REPORT, SHAPE A FAIRER WORLD!
Kansas City Chiefs
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.
The article uses sensational language to describe the performance of the Kansas City Chiefs.
The frightening part for the rest of the NFL is what they might look like once they hit their stride.
Use neutral language to describe the performance of the Kansas City Chiefs.
The article uses biased language to favor the Kansas City Chiefs.
The Chiefs beat the Minnesota Vikings 27-20 on Sunday.
Use neutral language to describe the outcome of the game.
The article fails to mention important details such as specific plays or key moments in the game.
The article provides a general overview of the game without diving into specific details.
Include specific plays or key moments in the game to provide a more comprehensive analysis.
- 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.