Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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Michael Jordan
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 dramatic language to provoke interest at the expense of accuracy.
The article uses phrases like 'almost suffocating', 'nearly electrocuted', and 'almost chopped off his toe' which may exaggerate the severity of the incidents to capture the reader's attention.
Use more neutral language to describe the incidents, such as 'experienced a suffocation risk', 'had a dangerous encounter with electricity', and 'suffered a toe injury'.
Attempting to manipulate an emotional response in place of a valid or compelling argument.
The recounting of traumatic events in Michael Jordan's childhood, such as 'witnessed one of his closest friends losing his life through drowning', is presented in a way that may be intended to elicit sympathy or admiration for his resilience.
Present the facts of Michael Jordan's childhood without language that is designed to elicit 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.