Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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Chapelton Maroons
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.
Use of slightly evaluative or value-laden wording that implies a positive judgment rather than strictly neutral description.
The phrase: "before the dependable Tajay Grant earned a point for Racing when he scored in the 75th minute." The word "dependable" is a positive evaluative adjective that goes beyond pure fact and subtly frames the player in a favorable light. While common in sports reporting and very minor, it is not strictly neutral.
Replace "the dependable Tajay Grant" with a neutral description such as "Tajay Grant" or "midfielder Tajay Grant".
If the intent is to justify the descriptor, add factual support: for example, "Tajay Grant, who has scored in four of Racing’s last six matches, earned a point for Racing when he scored in the 75th minute."
Maintain a consistent level of descriptive language for all players and teams (e.g., avoid positive adjectives for one side unless similar factual context is provided for others).
- 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.