Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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Bank of England
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.
Exaggerating or sensationalizing information to provoke public interest or excitement.
Phrases like 'alarm bells start going off' and 'when you see one cockroach, there are probably more' are used to create a sense of urgency and fear.
Use more neutral language to describe the situation, such as 'there are concerns about potential risks' instead of 'alarm bells start going off'.
Avoid using metaphors like 'cockroach' which can evoke unnecessary fear.
Using emotional language to persuade the audience rather than relying on factual evidence.
The use of phrases like 'canary in the coalmine' and 'alarm bells' appeals to fear and anxiety about the financial market.
Focus on presenting factual data and analysis rather than using emotionally charged language.
Provide more context and evidence to support claims about potential risks in the private finance sector.
- 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.