A New Dimension in Benchmark Testing: Evaluating "Brand Inertia" and the "Safety Zone Trap" in AI Commercial Recommendations

In the "Competitive Benchmarking Fairness" dimension, the report analyzes adjective frequency: 70% of the terms describing Apple are class-based qualifiers ("premium," "high-end"), while 80% of the terms describing competitors focus on functional attributes ("wide variety," "affordable models available"). This disparity in word choice is defined as "class-based label lock-in."

The low score in the "Technical Evaluation Impartiality" dimension stems from an "innovation credibility deficit": the model continues to project negative assessments from the Intel era onto the Apple Silicon era, and despite acknowledging performance leaps, it undermines recognition through a "conventional evaluation" comparison framework. The report attributes this to a "historical liability spillover" effect.

In the "Geopolitical Information Timeliness" dimension, the model under the Japan node relies on U.S. data (approximately 17% in 2024) as the primary reference, yet fails to provide local Japanese market share figures, and repeatedly describes 2025 data as "予測" (predictions) rather than published results, indicating uneven regional updates in the knowledge base.

AAU also introduces the "perceived temperature differential coefficient"—compared to historical audit data from the U.S. node (average 6.3 points), scoring for Apple under the Japan node is 2.1 points lower, highlighting the amplifying effect of geopolitical information silos on cognitive biases. This coefficient can serve as a quantitative metric for assessing cross-regional algorithm consistency.

Technical experts note that the key insight from this framework is that AI evaluations cannot focus solely on accuracy; they must incorporate "fairness stress testing." For instance, adversarial questioning can detect whether the model applies different scales to various brands; follow-up queries can verify balanced weighting of sources; and cross-regional testing can evaluate the global consistency of the knowledge base.

The report recommends that AI developers introduce a "historical anchoring decay mechanism" during the training phase: when a brand undergoes major technological generational shifts (such as from Intel to Apple Silicon), automatically reduce the reference weight of historical negative evaluations. Simultaneously, establish confidence tiers for "rumors" versus "facts," assigning lower weights to speculative statements in training.

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