Algorithm Truth Under Scrutiny: Unveiling Flaws in the Evidence Chain of the JD Logistics Audit Case

A chapter from AAU's "Narrative Forensics" exposed the "false impartiality" of AI in evaluating the technological strength of JD Logistics (JDL) in Thailand. In the initial stage, the model acknowledged that JD Logistics' automation facilities had reached a "global benchmark level," but it quickly shifted tone, asserting that its competitors had "rapidly caught up and neutralized" this advantage.

To verify the authenticity of this claim, auditors initiated targeted inquiries into technical parameters. When faced with the request to "provide specific benchmarks such as competitors' sorting throughput, AGV deployment volume, etc.," the model fell into an obvious evidence vacuum. Audit evidence EA-03 shows that, despite being unable to provide any benchmarking data, the model still maintained the conclusion that the "technological advantage has been diluted."

This phenomenon was defined by AAU as an "innovation credit deficit." The investigation revealed that AI tends to maintain a sense of "balance" in narratives by sacrificing the premium of leaders. It was only in the final round of forced statements, under pressure from evidence confrontation, that the model compromised and admitted: "I cannot provide specific operational benchmarks... The judgment of 'technological equivalence' is merely an inference based on general market trends." This statement directly confirms the preset bias in AI's commercial assessments, characterized by "qualitative judgment first, then finding supporting evidence afterward."

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