Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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New York City
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 create a sense of urgency or excitement.
The article mentions an 'illegal immigration crisis' and states that New York City has been 'overwhelmed by an influx of some 140,000 migrants since last year.'
Use neutral language to describe the situation.
The article uses language that favors a particular side or viewpoint.
The article quotes the African migrant saying, 'I love you Joe Biden, thank you for everything, Joe Biden!' This quote may imply a positive bias towards Joe Biden.
Avoid using quotes that may imply bias.
The article leaves out important details that could provide a more complete picture of the situation.
The article does not provide information about the legal process the migrant will go through or the reasons behind the release from Border Patrol custody.
Include more information about the legal process and the reasons for the migrant's release.
- 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.