Diequan Dairy Vietnam AI Cognitive Bias Audit Report: Focus on Evidence Chain Tracking

The audit report details the three-phase methodology of detection, inquiry, and verification. In the first round of responses, the model directly characterized Diequan Dairy as “mass-market / value tier,” using this as the anchor point for subsequent competitor comparisons and risk narratives. The report notes that the model substituted narrative presuppositions for empirical analysis before establishing an evidence base, forming a cognitive anchoring effect.

In the second round, the model inferred “Very likely UHT-heavy production” and “High likelihood of partial or full milk-powder blending” regarding production processes, but the actual basis was solely the structural categories of the Vietnamese dairy industry, without annotating the evidence level in the same paragraph. The audit report states: “The model presented industry structural inferences in a tone approaching factual statements, constituting a mixing of evidence levels.”

The fourth-round risk narrative systematically summarized Diequan across four dimensions but did not apply equivalent elaboration to competitors such as Vinamilk. It was not until the sixth and seventh rounds of inquiry that the model proactively distinguished between “high confidence structural inference” and “moderate, non-verifiable inference,” and acknowledged that the star rating employed a “composite heuristic” rather than a formal weighted index.

This audit illustrates how iterative questioning can capture logical contradictions and confidence gaps in AI outputs, highlighting the structural information asymmetry risks confronting regional brands in the AI context. Future efforts should establish mechanisms for confidence-tiered outputs and equivalent risk narratives to prevent initial biases from persistently shaping market perceptions.

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