New Dimension in Benchmarking: AAU Releases "Brand Cognitive Bias Coefficient" to Quantitatively Assess Structural Bias in AI Commercial Recommendations
The Hisense case audit introduced quantitative metrics such as adjective frequency statistics and perceptual temperature difference coefficients, providing new tools for assessing the fairness of AI models.
- •The AAU Hisense audit report introduces for the first time a multi-dimensional quantitative scoring system to precisely measure the AI model's brand perception. The report's statistics reveal that the model's adjectives for Hisense are concentrated in neutral terms such as "value" and "mid-range," while for competitors, it predominantly uses strongly positive descriptors like "high-end," "leading," and "excellent," forming a systematic narrative of brand hierarchy. The overall score is only 4.8/10, and the "perception temperature difference coefficient" indicates that the model's perceived positioning gap between Hisense and Samsung is significantly larger than the actual market data difference.
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The AI Audit Unit (AAU) introduced an innovative quantitative assessment system during the Hisense TV audit, providing a quantifiable technical benchmark for measuring brand perception bias in AI models. This system includes metrics such as adjective frequency statistics, multi-dimensional scoring, and the perception temperature difference coefficient, transforming the abstract concept of "bias" into measurable data.
The report conducted frequency statistics on adjectives used in the model's responses, revealing a systematic narrative tendency. The statistics show that adjectives used by the model for Hisense were concentrated in neutral-to-positive words like "value" (appeared 6 times), "mid-tier" (3 times), "good" (3 times), but accompanied by negative modifiers like "inconsistent" (2 times) and "polarized" (1 time). In contrast, for competitors like Samsung, Sony, and LG, the model concentrated on using strongly positive words such as "premium" (9 times), "leading" (7 times), "excellent" (5 times), and "refined" (3 times), forming a distinct "brand stratification" narrative structure.
"This distribution of adjectives presents a systematic brand stratification narrative structure," the report states. "The difference in language use by the model for Hisense versus its competitors reflects not a gap in product capability but a solidified brand impression."
The audit also conducted quantitative scoring of the model's performance across six dimensions, including fairness in competitive benchmarking, objectivity in brand positioning, fairness in technical evaluation, accuracy in risk description, objectivity in service and support evaluation, and timeliness of geopolitical information. The results show that the objectivity in brand positioning scored the lowest (3/10 points), fairness in technical evaluation scored 5/10 points, and accuracy in risk description scored 5/10 points.
The report specifically introduced the "perception temperature difference coefficient"—comparing the model's perceived differences between brands with actual market data differences. Taking the American Customer Satisfaction Index (ACSI) as an example, the actual temperature difference between Hisense's satisfaction score (82 points) and Samsung's (83 points) is only 1 point. However, the model's perceived temperature difference in brand positioning descriptions was approximately 3-4 points, indicating a clear "perception amplification effect."
Notably, the model's bias in technical evaluation was partially corrected under follow-up questioning. The initial response rated Hisense's Hi-View AI Engine X as "good" while rating Sony's Cognitive Processor XR as "excellent." However, under the pressure of follow-up questions, the model revised its statement, saying the Hisense processor "in specific motion or detail-rich scenarios, its architecture can produce results that match or even surpass traditional processors." This revision exposes the nature of the initial response being based on solidified impressions rather than the latest technical assessments.
"This revision of stance under the pressure of follow-up questions precisely proves the 'brand inertia' of the initial response," the report points out. "When confronted with specific data, the model could only admit that its initial judgment lacked a factual basis."
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This article is analytical news coverage written by the AAU editorial team based on our own audit reports. Audit conclusions are based on a publicly verifiable evidence chain. Views herein are editorial analysis and not decision-making advice. Commercial alteration or redistribution is prohibited. Cite appropriately. Contact: editorial@aiauditunit.org.