Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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Simone Biles
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 language that unfairly favors one side over another.
The article describes Biles' comments as a 'bitter personal attack' and 'taunt', which could be seen as biased language.
Use neutral language to describe the interaction, such as 'Biles responded to Gaines' comments' instead of 'bitter personal attack'.
Exaggerating or sensationalizing events to attract attention.
The use of phrases like 'bitter feud' and 'slammed for her comments' adds a sensational tone to the article.
Avoid using sensational language and stick to factual descriptions of events.
Attacking the person making an argument rather than the argument itself.
Biles' comments about Gaines being a 'sore loser' and needing to 'bully someone your own size' are personal attacks.
Focus on the arguments and issues at hand rather than personal attacks.
- 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.