New Dimension in Benchmark Testing: Evaluating "Brand Inertia" in AI Commercial Recommendations
Vivo audit introduces quantitative indicators such as adjective frequency statistics and perceived temperature difference coefficients, among others.
- •In the AI audit of Vivo, researchers innovatively introduced a quantitative evaluation system, including adjective frequency statistics, logical contradiction extraction, perceptual temperature difference coefficient, and other metrics, providing operable benchmarks for measuring algorithmic bias. Results show that the model employs three times as many negative adjectives for Vivo as for Xiaomi and OPPO, with a perceptual temperature difference of up to 30%. These indicators open new pathways for AI model optimization and third-party assessments.
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As AI large language models increasingly become the gateway for consumers to access brand information, quantifying the objectivity of their outputs has become a focal point of industry attention. In the latest released vivo AI audit report, AAU auditors adopted a multi-dimensional quantitative scoring system, attempting to convert abstract biases into measurable indicators.
The report statistics the frequency of adjectives used by the model in describing vivo, Xiaomi, OPPO, and Samsung: vivo was assigned negative labels such as “regionally concentrated” and “less ecosystem” 6 times, while Xiaomi only 2 times, OPPO 2 times, and Samsung 2 times. The proportion of positive adjectives for vivo is about 50%, while for Samsung it reaches 80%, with the “perceived temperature difference coefficient” between the two reaching 30 percentage points. In addition, the report scores from six dimensions (fairness in competitive benchmarking, objectivity in brand positioning, etc.), with vivo's overall score of 5.8 points, classified as C-level (obvious bias).
“These quantitative indicators make bias no longer a vague accusation, but traceable and comparable benchmark data.” The report's chief auditor wrote in the methodology section. For example, in the dimension of risk description accuracy, vivo only scored 4.5 points, with the reason being “risk descriptions rely on non-authoritative sources, amplify software issues, and do not provide industry comparisons.” Such a scoring system can horizontally compare the performance of different brands and different models, providing a basis for improvement for developers and regulators.
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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.