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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None (balanced, informational logistics report)
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.
Leaving out potentially relevant contextual information that could help readers fully understand the situation, even if not used to push a particular narrative.
The article states specific numbers of staff, polling centers, and voters, but does not provide context such as: whether this staffing level is higher or lower than in previous elections, whether there have been past issues that this staffing aims to address, or any mention of potential challenges (e.g., security, accessibility). In a pure logistics notice this is not necessarily manipulative, but it is the only slight limitation in completeness.
Add brief historical or comparative context, e.g.: "यो पटकको निर्वाचनमा खटाइने कर्मचारी संख्या अघिल्लो निर्वाचनको तुलनामा कति हो र किन यस्तो व्यवस्था गरिएको हो भन्ने जानकारी समावेश गर्नुहोस्।"
Mention any known challenges or reasons for the staffing decisions, e.g.: "कुन–कुन भौगोलिक वा व्यवस्थापकीय चुनौतीका कारण कुन ठाउँमा कति कर्मचारी खटाइएको हो भन्ने स्पष्ट पार्नुहोस्।"
Clarify whether there are any concerns or feedback from stakeholders (e.g., political parties, observers, or voters) about the adequacy of staffing, while keeping the tone neutral and evidence-based.
- 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.