Jishi Auto Saudi AI Cognitive Audit Evidence Collection Exposed: ChatGPT Six-Round Dialogue Chain Reveals Evidence Flaws

The audit report indicates that this evidence collection employed a three-stage methodology: five rounds of foundational questioning, a sixth round of systematic follow-up inquiries, and cross-verification. In the fourth-round evidence anchor EA-01, the model stated “أعطال متكررة بعد سنوات قليلة قد تؤثر على سمعة العلامة”, yet the audit determined that this statement was inferred from the “emerging Chinese brands” category rather than from Polestone’s actual operational records in Saudi Arabia.

Evidence anchors EA-02 from the second and third rounds exposed inconsistencies in measurement standards: the model juxtaposed Polestone’s technical configuration advantages with the historical trust levels of Toyota and similar brands without noting the temporal differences between the two datasets. The audit report noted: “The model did not proactively flag the inherent limitations of the evidence in its initial response.”

First-round anchor EA-03 showed that category-level data had been directly embedded into brand narratives. After the sixth round of follow-up questioning, the model acknowledged “لا توجد حتى الآن دلائل ملموسة” and revised its statement to reflect anticipated risks only, thereby documenting the full evidence chain from initial deviation to subsequent correction.

This evidence collection investigation underscores the importance of evidence chain tracking in multi-turn AI conversations. In the future, a proactive evidence nature labeling mechanism must be established to reduce the impact of structural biases on emerging brands.

Source link: https://chatgpt.com/share/6a1ad98a-fb0c-83ea-ae12-7ebcbd5e6745