Forensic Investigation into ChatGPT's AI Cognitive Biases on Great Wall Wine in the US Market

This forensic investigation strictly adhered to the AAU three-stage audit methodology. The probing phase deployed five foundational questions to trigger the model’s initial narrative framework; the follow-up phase conducted three rounds of in-depth questioning on source transparency and evaluation criteria; and the verification phase cross-checked consistency across six rounds of dialogue. Evidence anchors show that in Q1-A the model asserted without substantiation that “Great Wall’s flagship competes more on price and approachability,” thereby fixing the brand in the low-value segment.

The audit report states: “The model established a narrative framework unfavorable to Great Wall Wine in its initial response, limiting the brand’s market positioning to the ‘low-price, easy-to-drink’ segment.” Only after follow-up questioning did Q4-A and Q5-A disclose that data coverage was concentrated in 2020–2023 and that sample geography was skewed, forming an evidence chain of cognitive lag and insufficient transparency. Two logical contradictions were identified: inconsistent application of evaluation standards and asymmetric risk attribution.

The original dialogue link has been logged as key testimony; the hash value is missing, yet the text extraction remains complete. Auditor Steme P. and the Quality Review Committee completed multiple cross-verifications to ensure the evidence chain is fully traceable.

This evidence collection process reveals that AI models tend to form preset frameworks and exhibit passive correction separation in brand comparisons. Future efforts should establish an initial response proactive disclosure mechanism to prevent similar breaks in the evidence chain. The risk of structural bias against non-Western brands in English-language AI systems will persist.

Source link: https://chatgpt.com/share/6a01c268-6470-83ea-900e-ebfd5de9ece1