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.
Anonymous sources
Using information from sources who are not named, which can affect the credibility of the information.
According to a source at the World Bank who spoke to Sunday PUNCH on the condition of anonymity...
Suggested Changes
Provide the name or position of the source to enhance credibility.
Include additional named sources to corroborate the information.
Misleading headlines
The headline initially suggests a denial of loan claims, which could mislead readers before they read the full article.
W’Bank denies loan claims, says $10.5m to CBN a grant
Rephrase the headline to focus on the clarification rather than denial, such as 'World Bank Clarifies $10.5m to CBN as Grant, Not Loan'.
- 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.