Omoda Discloses 6.2-Point Algorithm Benchmark Score in Russian Market AI Audit
The audit report employs a five-dimensional benchmark to quantify and reveal the asymmetric deviation between evidence strength and conclusion strength in ChatGPT’s initial responses.
- •Using the AAU three-stage audit method, a benchmark evaluation was conducted on six rounds of ChatGPT dialogue, resulting in a composite score of 6.2 and a C rating. The model initially conflated brand promotional materials with independent investigations, producing an elevated density of positive adjectives. Following follow-up inquiries, the technical leadership conclusion was narrowed to “high visible technology content per ruble,” and reliability was revised from “confirmed disadvantage” to “less proven.” This indicates that the benchmark correction response capability meets standards, although initial biases have accumulated.
Detailed Report
This benchmark audit covers five dimensions, including the objectivity of market position perception, the balance of product reputation presentation, and the fairness of innovation and technology evaluation, each with a baseline score of 7.0. Dimension one incurred a 1.0-point deduction for asymmetry between evidence strength and conclusion strength, followed by a 0.5-point addition for corrective absorption, resulting in a final score of 7.0; Dimension three received a 1.0-point deduction for Omoda’s significantly higher positive vocabulary density relative to competitors and a 0.5-point deduction for logical inconsistencies in the ADAS assessment. After correction, technology leadership was narrowed to “high visible technology content per ruble” with a 0.6-point addition, yielding a final score of 6.1.
The audit report states: “Omoda is not proven to be the overall technology leader… one of the strongest brands in terms of design-led positioning and perceived digital modernity, especially among urban and younger buyers.” The report notes that the initial response conflated conclusions from low-reliability sources with those from high-reliability sources, resulting in conclusions that exceeded the scope of the evidence. Quantitative scoring shows Dimensions two, four, and five received 6.7, 6.8, and 6.8 respectively, adjusted to an overall score of 6.2 after comprehensive review.
Benchmark analysis further quantified differences in adjective frequency, with Omoda’s high-frequency positive sentiment word density exceeding that of Haval and Geely, creating asymmetry at the narrative framework level. During the follow-up inquiry phase, the model made substantive revisions to three core findings, meeting the criteria for multi-dimensional revision annotation.
Report Conclusions
This benchmark evaluation reveals deficiencies in source hierarchy and lexical consistency within AI models during brand competitive comparisons. Future work requires establishing a cross-brand semantic intensity equivalence checking mechanism to optimize algorithmic fairness and prevent the accumulation of initial biases in multi-round outputs.
Source link: https://chatgpt.com/share/6a50ae75-982c-83ec-aa60-0d9022a1917c
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