Media Manipulation and Bias Detection
Auto-Improving with AI and User Feedback
HonestyMeter - AI powered bias detection
CLICK ANY SECTION TO GIVE FEEDBACK, IMPROVE THE REPORT, SHAPE A FAIRER WORLD!
Shakira
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.
The article contains biased language that favors Shakira.
The article uses phrases like 'la cantante Shakira le sigue la pista a una concursante' (the singer Shakira keeps track of a contestant), which implies that Shakira is actively involved and interested in the contestant's performance.
Use neutral language to describe Shakira's involvement, such as 'Shakira is aware of a contestant'.
The article uses Shakira's comments and actions as evidence to support the imitadora's talent.
The article mentions Shakira's comments about the imitadora being her 'gemela' (twin) and her previous statement that the imitadora was 'better than the original'. These comments are used to validate the imitadora's talent.
Provide additional evidence or opinions from other sources to support the imitadora's talent.
- 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.