Media Manipulation and Bias Detection
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HonestyMeter - AI powered bias detection
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Food-delivery companies
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.
Presenting predictions or causal claims without showing evidence or specifying the basis for them.
The phrase "is likely to push up customer acquisition costs and slow new customer additions" is presented as a forecast without any data, examples, or explanation of the mechanism, beyond a generic attribution to "experts."
Add specific evidence or data: e.g., "Based on historical data from similar changes in delivery models, experts expect customer acquisition costs to rise by X–Y% and new customer additions to slow by Z% in the short term."
Clarify the basis of the prediction: e.g., "Experts interviewed by [source] argue that removing the 10-minute promise may reduce the immediate appeal for new users, which could push up customer acquisition costs and slow new customer additions."
Qualify the statement more clearly as a hypothesis: e.g., "Experts say this could push up customer acquisition costs and may slow new customer additions in the short term, although the extent of the impact remains uncertain."
Relying on unnamed or vague 'experts' as the main support for a claim, without clarifying who they are or why their view is credible.
The clause "according to experts" is used to support the prediction about costs and customer additions, but no information is given about who these experts are, their qualifications, or whether there is consensus.
Specify who the experts are: e.g., "according to analysts at [firm]" or "according to three industry consultants who advise major food-delivery platforms."
Indicate diversity or limits of opinion: e.g., "according to several industry analysts, though some company executives disagree with this assessment."
Provide at least one concrete, attributable quote or source: e.g., "according to [Name], a senior analyst at [Organization], who said, 'We expect...'."
Reducing a complex situation to a single or overly narrow set of causes or effects.
The sentence links phasing out 10-minute delivery directly to higher acquisition costs and slower customer additions, without acknowledging other factors (competition, pricing, marketing, macroeconomic conditions) that also influence these metrics.
Acknowledge other contributing factors: e.g., "This move is one of several factors that could influence customer acquisition costs and the pace of new customer additions."
Use more nuanced language: e.g., "may contribute to" instead of "is likely to push up," and "could slow" instead of stating it as a near-certainty.
Briefly note complexity: e.g., "While many factors affect customer acquisition and growth, experts say the removal of the 10-minute promise could add short-term pressure on these metrics."
- 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.