Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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None (sides are covered in a largely neutral, informational way)
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.
Using wording that subtly implies regret, loss, or blame rather than neutrally describing a transaction.
The sentence: "On Friday, the Cardinals let him slip from their fingers as he was taken by the Texas Rangers in a waiver claim, per Rangers PR." The phrase "let him slip from their fingers" suggests carelessness or a mistake by the Cardinals, which goes beyond the neutral fact that he was claimed on waivers. It introduces a mild emotional/interpretive framing instead of strictly reporting the transaction.
Replace with a neutral description of the transaction, e.g.: "On Friday, the Cardinals lost him on waivers when he was claimed by the Texas Rangers, per Rangers PR."
Or: "On Friday, the Texas Rangers claimed Kent off waivers from the St. Louis Cardinals, per Rangers PR."
Avoid idiomatic language that implies blame or regret and stick to straightforward transactional wording.
- 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.