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 emotion
The use of emotive language to create a positive sentiment.
The article uses positive language such as 'soulmates', 'amazing', 'great company', 'very smart', 'wonderful musicologist', and 'epic firework finale' which can evoke an emotional response and may not be necessary for conveying the information.
Suggested Changes
Use more neutral language to describe the relationship and achievements of Elton John and Brandi Carlile.
Avoid using superlatives unless they are directly quoted from a source.
Biased language
The use of language that implies a positive bias towards the subjects.
Phrases like 'one of the best things they’ve ever done', 'incredibly close', and 'everyone here is on his side' suggest a positive bias without providing evidence or a balanced viewpoint.
Provide evidence for claims of quality or success, or attribute these opinions to specific individuals.
Include any critical perspectives if they exist 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.