Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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Local Authority/Police
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 headline and the narrative suggest a more dramatic event than what the content supports.
Use a more accurate and less dramatic headline, such as 'Emma Watson's car towed due to parking violation'.
Headlines that are not fully supported by the article content or are exaggerated.
The headline implies a more confrontational or dramatic interaction with the police than what is described in the article.
Ensure the headline accurately reflects the content of the article.
Attempting to manipulate an emotional response in place of a valid or compelling argument.
The article includes details about Emma Watson's wealth and past career, which are not relevant to the parking incident but may evoke an emotional response from the reader.
Remove irrelevant details about Emma Watson's wealth and career that do not pertain to the parking incident.
- 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.