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!
Josh Allen
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 Patrick Mahomes' comments about the officials.
The article describes Mahomes' comments as 'expletive ridden' and highlights his frustration with the officials.
Use neutral language to describe Mahomes' comments.
The article uses biased language to favor Josh Allen's perspective.
The article portrays Allen as understanding and forgiving of Mahomes' comments, while emphasizing his sportsmanship.
Use neutral language to present both Allen and Mahomes' perspectives.
The article fails to mention the specific rule that Toney violated and the consequences of his actions.
The article mentions that Toney was offsides, but does not explain the specific rule he violated or the impact it had on the game.
Provide more information about the specific rule Toney violated and the consequences of his actions.
- 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.