HonestyMeter - AI powered bias detection
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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.
Sensationalism
Use of dramatic language to provoke interest at the expense of accuracy.
The article uses phrases like 'insensitive post' and 'shocking revelation' to sensationalize the situation.
Suggested Changes
Use neutral language to describe the post and the revelation of Kate Middleton's diagnosis.
Biased language
Language that is partial or prejudiced towards one side.
The article uses biased language such as 'piled on to speculation' and 'mad at the Kim Kardashians making fun of the situation,' which portrays Kardashian negatively.
Present the actions of Kim Kardashian and other celebrities without using language that implies judgment.
Omission of key information
Leaving out important details that could change the reader's perception.
The article does not include any response or comment from Kim Kardashian, which could provide her perspective on the situation.
Include any statements or apologies issued by Kim Kardashian to provide a more balanced view.
- 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.