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.
Sensationalism
Presenting the event in an exaggerated or attention-grabbing manner
The article uses sensationalism by emphasizing Mick Jagger's age (80) and comparing it to Lady Gaga's age (37) to create a sense of surprise and contrast.
Suggested Changes
Focus on the performance and collaboration between Mick Jagger and Lady Gaga without emphasizing their age difference.
Biased language
Using language that favors one side over the other
The article uses biased language by describing Lady Gaga as a 'pop songstress' and Mick Jagger as stealing the show, implying that he outperformed her.
Use neutral language to describe both Lady Gaga and Mick Jagger's performances.
Omission of key information
Leaving out important details that could provide a more complete picture
The article omits key information about the Rolling Stones' album Hackney Diamonds, such as its genre or critical reception.
Include more information about the album, such as its genre and critical reception.
- 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.