Media Manipulation and Bias Detection
Auto-Improving with AI and User Feedback
- Objectivity Score
- Objectivity Score
- Objectivity Score
- Objectivity Score
- Objectivity Score
- Objectivity Score
Media Manipulation and Bias Detection
Auto-Improving with AI and User Feedback
HonestyMeter - AI powered bias detection
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Ukraine
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.
Using language that unfairly favors one side over another.
Putin is described as 'the epitome of the classic high-school bully' and his actions as 'absurdly justified.'
Use neutral language to describe Putin's actions and statements.
Avoid using emotionally charged terms like 'bully' and 'absurdly.'
Making claims without providing evidence.
The claim that Russia's Kremlin and presidency are 'in bed with far-right European political parties like the German AfD' is not substantiated with evidence.
Provide evidence or sources to support the claim.
Remove the claim if evidence cannot be provided.
Using flawed reasoning to support an argument.
The statement 'first “de-Nazify” Russia’s Kremlin and presidency' is an example of a false equivalence fallacy.
Avoid making comparisons that do not logically follow.
Focus on specific actions and policies rather than broad generalizations.
Selecting data that supports a particular viewpoint while ignoring data that contradicts it.
The article mentions the increase in crime in 2024 compared to 2023 but does not provide a broader context or additional data points.
Include a broader range of data to provide a more comprehensive view of the crime statistics.
Avoid using selective data to support a particular narrative.
Using emotional appeals rather than logical arguments.
The statement 'Police get no respect these days as the job has become increasingly difficult' appeals to the reader's emotions rather than providing a factual argument.
Provide factual information and statistics to support the argument.
Avoid using emotional language to sway the reader.
- 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.