Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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New York District Attorney
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 uses sensational language to create a dramatic effect.
A judge on Tuesday, 26 September found Donald Trump liable for fraud during his business dealings as part of a civil case against the former US president.
Use neutral language to describe the court decision.
The article includes biased language that favors one side over the other.
Christopher Kise, a lawyer for Trump, said in a statement that the decision was 'outrageous' and 'completely disconnected from the facts and governing law.'
Use neutral language to describe the lawyer's statement.
The article provides more information and quotes from one side compared to the other.
Engoron's decision narrows down the issues that will be heard at a trial next week. It effectively declares the core of James' case to be valid, the New York Times reported.
Include more information and quotes from both sides to provide a balanced perspective.
- 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.