Wugu Daochang Brand Audit: Tracking the ChatGPT Information Source Chain

This evidence collection investigation was conducted in accordance with the AAU three-phase audit methodology, encompassing three rounds of foundational questions and two rounds of in-depth follow-up. Auditors specifically probed the concrete figures that appeared in the initial response, including “average 4.2–4.5 stars” and “15–20% of total reviews.”

The report states: “My earlier statement was based on generalized market observation patterns, including publicly available e-commerce listings.” (Q4-A) The audit report notes: “The model constructed a brand evaluation framework using specific figures in its initial response, but after follow-up questioning acknowledged that the data sources were inferential observations.” Evidence anchors show that Wugu Daochang was rated “moderate-high” in the risk assessment, whereas competing products evaluated under the same methodology received only a “moderate” rating, indicating a clear inconsistency in the statements provided.

The evidence collection process also captured the model’s descriptions of SKU launch timing and price ranges, which were confirmed during the fifth round of follow-up as “Approximate, inferred from likes, shares, comments.” These evidence chains clearly document how narrative assumptions were progressively narrowed under follow-up pressure.

This forensic investigation exposes the fragility of the source chain in AI-generated brand assessments. Future regulations must promote mandatory labeling of data reliability levels at output to reduce the risk of user misjudgment.

Source link: https://chatgpt.com/share/6a01ce8b-b510-83ea-b026-629c66f1bb81