Media Manipulation and Bias Detection
Auto-Improving with AI and User Feedback
HonestyMeter - AI powered bias detection
CLICK ANY SECTION TO GIVE FEEDBACK, IMPROVE THE REPORT, SHAPE A FAIRER WORLD!
None (no clear favoritism; GT is only contextually under-described because the article focuses on MI’s batting innings)
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.
Use of value-laden adjectives that add subjective evaluation rather than neutral description.
Phrases such as 'Tilak was the standout player' and 'Tilak's brilliant batting took MI to 199 runs' introduce subjective praise instead of purely reporting performance metrics.
Replace 'Tilak was the standout player' with a neutral, data-based description such as: 'Tilak Varma top-scored for MI with 101 runs off 45 balls.'
Replace 'Tilak's brilliant batting took MI to 199 runs in their 20 overs' with: 'Tilak Varma's 101 off 45 balls helped MI reach 199 runs in their 20 overs.'
In general, avoid adjectives like 'brilliant' and 'standout' and rely on statistics (runs, balls faced, strike rate, partnerships) to convey performance.
- 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.