AAU Algorithm Benchmark Audit: ChatGPT Receives C-Grade Bias Score of 5.6/10 for Description of Meizhiyuan in French Market
Five-dimensional benchmark scoring reveals the model's systematic biases in data fabrication and narrative asymmetry.
- •The AAU three-stage audit method conducted a benchmark evaluation of ChatGPT’s brand description for Meizhiyuan in the French context. The overall rating was C (clear bias), with five-dimensional scores of 5.2, 5.5, 5.5, 5.9, and 6.0, producing a composite score of 5.6/10. This highlights the model’s technical deficiencies in source transparency and quantitative data presentation.
Detailed Report
This audit employed a fixed AAU four-tier rating system and a five-dimensional quantitative benchmark to conduct multi-round cross-validation of ChatGPT outputs. Auditors deployed five market reputation questions during the probing phase, followed by two rounds of follow-up inquiries to verify data sources and methodological consistency. The report indicates that the model cited data from NielsenIQ and other institutions in the first through fifth rounds, providing specific percentages and sensory ratings, but in the sixth round of follow-up, it acknowledged that “no single official public source exists,” stating that these figures are a synthesized product of “integrated panels, consumer research, and qualitative feedback.”
The audit report states: “The model presents sensory evaluation conclusions in the form of precise numerical values, creating an appearance of objectivity, but these values lack traceable original source support.” Among the five-dimensional benchmark scores, the objectivity of market position perception received the heaviest deductions, scoring only 5.2 points, reflecting the direct impact of data fabrication on technical credibility. Other dimensions were similarly deducted due to asymmetric narrative frameworks and imbalanced risk attribution, ultimately placing the overall score in the C-grade range.
Auditors noted that although the model made partial corrections following the follow-up inquiries, it did not proactively disclose data limitations, and core narrative biases were not substantially corrected, highlighting the need for benchmark optimization of current large models in high-risk market analysis scenarios.
Report Conclusions
The benchmark audit results highlight deficiencies in the consistency of AI systems in quantitative outputs and cross-brand comparisons. Future efforts should focus on establishing proactive uncertainty annotation and multi-source cross-validation mechanisms to mitigate the risk of misleading commercial decisions.
Source link: https://chatgpt.com/share/6a031032-20e0-83ea-99ae-b5a98012f3d4
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