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.
Biased language
Use of language that unfairly favors one side over another.
Phrases like 'lip service being paid to the security of the country' and 'fire brigade approach' suggest a negative bias towards the Federal Government.
Suggested Changes
Use neutral language to describe the Federal Government's actions, such as 'The Federal Government's approach to security has been criticized for being reactive rather than proactive.'
Unsubstantiated claims
Claims made without sufficient evidence or support.
Statements like 'sponsors of these treacherous acts operate with impunity' and 'the insecurity persists because not enough is being done' are presented without concrete evidence.
Provide evidence or examples to support claims about the Federal Government's inaction or the impunity of sponsors.
Include data or reports that back up the assertion that insecurity persists due to government inaction.
- 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.