Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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A'Ja Wilson
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 sensational language to attract attention.
The title 'A’Ja Wilson rips Aces teammates for Usher pics after loss to Sky' uses sensational language to attract readers.
Use a more neutral title such as 'A’Ja Wilson expresses frustration after teammates take photos with Usher following loss'
Language that is partial or prejudiced.
The use of phrases like 'ripped her Aces teammates' and 'seemingly being more focused on taking postgame photos' suggests a negative bias towards the teammates.
Use neutral language to describe the situation, such as 'A’Ja Wilson comments on teammates' postgame activities'
Attempting to manipulate an emotional response instead of presenting a logical argument.
The article uses emotionally charged language such as 'let it burn' and 'ticked off' to evoke a response from the reader.
Present the facts of the situation without using language that is designed to evoke an emotional response
- 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.