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!
Company/CEO (Henan Kuangshan Crane / Cui Peizun)
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 emotionally charged framing or quotes to create a positive or negative impression without adding substantive evidence or counterpoints.
The article includes the CEO’s quote: “ფულის დარიგება უყვარს, განსაკუთრებით ახალგაზრდების დახმარება, რომელებსაც მანქანის სესხები და იპოთეკური ვალდებულებები აწევთ კისერზე და მათ მიმართ ნებისმიერი დახმარება, მნიშვნელოვანია“ and states that the company has a “გულუხვობის ხანგრძლივი ისტორია” (a long history of generosity) without any critical or neutral balancing context. This framing invites admiration and sympathy for the CEO and company, emphasizing generosity and concern for indebted youth, but does not examine potential PR motives, sustainability, or whether all employees benefit equally.
Rephrase evaluative language into neutral description, e.g. instead of “კომპანიას თანამშრომლთა მიმართ გულუხვობის ხანგრძლივი ისტორია აქვს” use “კომპანიამ წარსულშიც არაერთხელ გასცა მაღალი ბონუსები თანამშრომლებისთვის” and support it only with the listed figures.
Contextualize the CEO’s quote by clearly attributing it and distinguishing it from the outlet’s voice, e.g. “პეიძუნის თქმით, მას ‘ფულის დარიგება უყვარს’ და განსაკუთრებით ახალგაზრდების დახმარება სურს… მედია დამოუკიდებლად ვერ ამოწმებს, რამდენად თანაბრად ნაწილდება ეს დახმარება თანამშრომლებს შორის.”
Add neutral or critical context where relevant, such as information on average wages in the company or sector, or expert commentary on such bonus practices, to reduce the purely emotional impact of the CEO’s self-portrayal.
Presenting only one side or only positive aspects of a story while omitting relevant context or potential downsides.
The article exclusively highlights large bonus sums, cash on tables, and the CEO’s stated generosity. It does not mention any potential criticisms (e.g., whether such one-off bonuses substitute for higher base pay, whether all employees benefit equally, whether there are concerns about safety or legality of large cash distributions, or whether this is part of a PR strategy). This creates a one-sided, celebratory narrative of the company and its leadership.
Include information on average salaries, working conditions, or typical bonus practices in the industry or region to contextualize whether these bonuses are exceptional or compensate for other shortcomings.
If available, add perspectives from employees (both positive and, if they exist, critical) about how such bonuses affect their financial situation and job satisfaction, clearly attributing each view.
Mention any relevant regulatory or expert commentary (e.g., on tax, security, or labor-relations aspects of distributing $26 million in cash) to show that the practice is being examined from more than one angle.
Clarify that the article is based on company-provided information if that is the case, e.g. “ინფორმაცია კომპანიის განცხადებაზე/ვიდეოზეა დაფუძნებული, დამოუკიდებელი დადასტურების გარეშე.”
Allowing one positive trait or action to create an overall positive impression that is not fully supported by broader evidence.
By emphasizing spectacular cash bonuses and describing a “გულუხვობის ხანგრძლივი ისტორია”, the article implicitly suggests that the company is generally benevolent toward employees. No other aspects of the company’s behavior (e.g., labor disputes, working hours, safety record) are discussed, so the reader may generalize from the highlighted bonuses to an overall positive evaluation of the company.
Explicitly limit the scope of positive claims, e.g. “ბონუსების მოცემული მაგალითები მიუთითებს, რომ კომპანია პერიოდულად გასცემს დიდ პრემიებს, თუმცა სტატია არ ეხება სხვა სამუშაო პირობებს ან კომპანიის მთლიან პოლიტიკას.”
Add neutral background on the company’s broader track record (if available), including both positive and negative aspects, so that the reader does not infer overall virtue solely from the bonus events.
Avoid broad characterizations like “გულუხვობის ხანგრძლივი ისტორია” unless supported by multiple independent sources and clearly defined criteria.
- 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.