Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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Kendall Jenner
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 grab attention and create excitement.
The article describes Kendall Jenner's Halloween costume as 'channeling' Marilyn Monroe and emphasizes the 'striking resemblance' between them.
Use more neutral language to describe Kendall Jenner's costume and resemblance to Marilyn Monroe.
The article uses language that favors one side or presents a subjective opinion.
The article refers to Kendall Jenner as a 'star' and 'supermodel,' which can create a positive bias towards her.
Use more neutral language to describe Kendall Jenner's profession and avoid creating a bias.
The article leaves out important details that could provide a more complete picture.
The article mentions Kendall Jenner's themed Halloween bash and the attendance of other celebrities, but does not provide any further information about the event.
Include more details about Kendall Jenner's Halloween party and the significance of other celebrities attending.
- 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.