Media Manipulation and Bias Detection
Auto-Improving with AI and User Feedback
- Objectivity Score
- Objectivity Score
- Objectivity Score
- Objectivity Score
- Objectivity Score
- Objectivity Score
Media Manipulation and Bias Detection
Auto-Improving with AI and User Feedback
HonestyMeter - AI powered bias detection
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Critics
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 suggests that all fans are slamming the clothing line, which may not be accurate.
Rephrase the headline to reflect that 'Some Kylie Jenner fans express concerns over clothing line inclusivity'.
Language that is biased towards one side or another, often lacking neutrality.
Phrases like 'fume it's only for slim people' and 'clothes for skinny people and only skinny people' suggest a bias towards the negative feedback without providing a balanced view.
Use more neutral language to describe the situation, such as 'concerns raised about inclusivity of clothing line'.
Attempting to manipulate an emotional response in place of a valid or compelling argument.
Comments like 'These clothes will not look good on you without editing. Sorry girls.' are designed to evoke an emotional response from the reader.
Provide a more balanced view by including statements from the brand or fashion experts about the design and target audience of the clothing line.
A headline that does not accurately reflect the content of the article.
The headline implies a general consensus of disapproval, which is not fully supported by the article content that also includes positive feedback.
Adjust the headline to more accurately reflect the content, such as 'Mixed reactions to Kylie Jenner's new clothing line'.
- 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.