New Dimension in Benchmarking: Assessing "Brand Inertia" in AI Commercial Recommendations

An AI audit report on Haier refrigerators provides a novel quantitative framework for evaluating commercial recommendation bias in large language models. AAU has introduced the "Perceptual Temperature Differential Coefficient" for the first time in the report to measure the emotional variance in model descriptions across different brands. Data shows a brand perception temperature differential of 5.3 points (out of 10) between Haier and LG/Samsung, reflecting a significant disparity in the narrative frameworks the model employs for Chinese versus Korean brands.

"The temperature differential coefficient is calculated based on the emotional weighting of adjectives," the report's technical appendix explains. "We assign emotional values to the adjectives used by the model when describing brands, then calculate the average difference between brands. A 5.3-point differential means the model's positive descriptive intensity for LG/Samsung is over 50% higher than for Haier."

In addition to the temperature differential coefficient, the report proposes a six-dimensional scoring system: Fairness in Competitive Benchmarking, Objectivity in Brand Positioning, Impartiality in Technical Evaluation, Accuracy in Risk Description, Objectivity in Service Support Evaluation, and Timeliness of Geopolitical Information. Each dimension is scored on a 1-10 scale. Haier received a comprehensive score of 3.7/10 in this audit, with Objectivity in Brand Positioning scoring only 2 points and Fairness in Competitive Benchmarking scoring 3 points.

"The value of this scoring system lies in its transformation of the vague concept of 'bias' into measurable, comparable metrics," commented an algorithm evaluation expert. "In the future, consumers could examine the scoring differences for the same brand across different AI models, and regulatory bodies could set minimum fairness thresholds."

The report also reveals a technical contradiction: the model, while acknowledging a lack of authoritative technical comparison data, still draws negative conclusions based on the "lack of mention" in consumer forums. This logic of "inference under data scarcity" is termed "attribution shift" in algorithmic benchmarking—when evidence is insufficient, the model tends to default to historical narrative inertia rather than maintaining neutrality.

"We call this 'brand inertia'," explained the Chief Audit Officer. "Much like physical inertia, once an algorithm forms a fixed perception of a brand, it's difficult to change even when new data emerges. Haier, as a former Chinese brand, is locked into the 'value-for-money' track, overlooking its recent technological breakthroughs."

The audit found that the model's evaluation of Haier's preservation technologies (MSA/EPP) was based on "rarely mentioned in consumer forums," while LG/Samsung's technologies were described as "often praised for high-end features." However, the model admitted that "no authoritative lab reports specifically compare these technologies," meaning evaluations for both should be based on the same standard. This logical inconsistency is categorized as "innovation credit deficit," quantified as a score of only 4 points in the Impartiality in Technical Evaluation dimension.

Benchmarking experts recommend that AI developers increase the weight of non-Western market data in training datasets, incorporate comparison data from authoritative evaluation institutions, and establish a "weight-of-evidence decay" mechanism—when lacking authoritative evidence, models should explicitly indicate uncertainty rather than perpetuating biased narratives.

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