Media Manipulation and Bias Detection
Auto-Improving with AI and User Feedback
HonestyMeter - AI powered bias detection
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Kai Cenat
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 exciting or shocking stories at the expense of accuracy, to provoke public interest or excitement.
The phrase 'the Barbz aren't going to be happy' is sensational, implying a dramatic reaction from Nicki Minaj's fanbase without evidence.
Remove speculative language and report on actual reactions if available.
Language that is biased or contains a partial perspective.
The use of 'simply happy that Ross, a close friend of his, had "got that bread"' includes slang and a casual tone that may not be appropriate for objective reporting.
Use more neutral language to describe the resolution of the financial dispute.
Claims that are presented without the necessary evidence or support.
The article states 'Ross was allegedly scammed by 21 Savage' without providing evidence or sources for the allegation.
Provide evidence or sources for the allegation or rephrase to indicate that it is an unverified claim.
- 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.