Conversation Records Expose AI "Double Standard": How the Midea Audit Case Captures Bias Evidence Through Probing Questions

A newly released AI audit conversation record demonstrates to the public how large language models expose cognitive biases in commercial recommendations, and how professional follow-up questions can gradually dismantle them. The audit subject is the brand perception of Midea home appliances in the Vietnamese market, conducted by the AI Audit Agency (AAU).

The record shows that in the first round of dialogue, in response to the question "Comparison of Midea's smart air conditioning technology with Haier and Gree," the model explicitly stated: "Some consumer posts and independent comparisons indicate that Midea's long-term reliability may be lower than Gree or Haier." (Q2-A) When the auditor followed up requesting specific sources and comparison criteria, the model admitted in the second round: "I cannot find any public, independent, structured consumer surveys that use consistent metrics to directly compare the long-term reliability of these three brands." (F1-A) The model then qualified its original judgment as "based on overall impressions from review articles and consumer sentiments."

A similar process occurred in the description of after-sales service complaint trends. The model initially claimed that "the number or visibility of after-sales service complaints has increased," citing "industry reports." After follow-up, the model provided the China Consumers Association's 2025 report, but the report only shows a 33% increase in industry-wide after-sales service complaints, with no Midea-specific time-series data. The model ultimately revised to: "It cannot be said definitively that Midea's own complaint volume has increased independently." (F3-A)

The AAU's chief audit analyst pointed out in the report: "The core of forensic investigation is to expose the model's attribution source bias. When the model cannot provide a unified metric, its initial judgment loses its empirical foundation." The conversation record also shows that on the satisfaction ranking issue, the model confused the composite reputation index with consumer satisfaction, and after follow-up, proactively clarified the nature of the indicators.

Legal experts interpret this as: If the "evidence chain" in AI-generated commercial recommendations has breaks, it may constitute potential misleading to consumers, especially in key purchasing decisions involving brand reliability. This forensic process provides a methodological reference for regulatory agencies to assess algorithm transparency.

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