HonestyMeter - AI powered bias detection
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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.
Appeal to authority
Using the opinion of an authority figure to support a claim.
Goldberg said she wouldn’t hear anything of it. 'They apparently forgot that they won a Super Bowl last year with [Kelce], and [he and Swift] were just as tight then as they are now,' Goldberg said.
Suggested Changes
Include more balanced viewpoints from other experts or fans to provide a more comprehensive perspective.
Biased language
Using language that unfairly favors one side over another.
'Grow up, y’all. Stop putting this on her. Your team is fine, your team is doing well. Shut up.'
Use neutral language to describe the situation without telling the audience how to feel.
Unbalanced reporting
Giving more weight to one side of the argument.
The article primarily focuses on Goldberg's defense of Kelce and Swift, with less emphasis on the criticisms from fans and analysts.
Provide more detailed coverage of the criticisms and concerns from fans and analysts to balance the reporting.
- 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.