Media Manipulation and Bias Detection
Auto-Improving with AI and User Feedback
HonestyMeter - AI powered bias detection
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Margot Robbie and America Ferrera
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 sensational language to provoke interest at the expense of accuracy.
The article title uses sensational language to draw attention to a controversy that may not be as significant as portrayed.
Use a more neutral title that accurately reflects the content of the article.
Headlines that do not accurately reflect the content of the article.
The headline suggests that Robert Downey Jr. is being widely criticized for his comments, which may not be the case based on the content provided.
Rewrite the headline to more accurately reflect the content and tone of the article.
Language that is partial or prejudiced towards one side.
The phrase 'people are calling him out' suggests a widespread negative reaction without providing sufficient evidence of such a consensus.
Provide evidence for the claim or rephrase to indicate that some people, not all, are reacting negatively.
- 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.