Conversation Logs Expose AI "Class Locking": Five Rounds of Questioning Capture Evidence Chain of Apple Brand Bias
AAU Auditor Reveals How to Make Models Self-Demonstrate Source Imbalances and Logical Contradictions Through Layered Questioning
- •AAU Audit Report First Publicly Discloses Forensic Details: Through five rounds of basic questioning and three rounds of in-depth probing, auditors gradually captured the AI's "class-based label locking" and "rumor factualization" behaviors toward Apple Mac. When probing the source of the GPU core count, the model initially cited speculative rumors, only admitting that official specifications were already clear after being requested to provide specific sources. The complete conversation chain serves as irrefutable evidence of algorithmic bias.
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How to Prove Systemic Bias in AI? The AAU Audit Office adopted the "Three-Stage Audit Method" in this Apple audit: probing, questioning, and verification. By publicly sharing the original conversation links, the bias evidence chain becomes traceable and verifiable.
Auditors first designed five neutrality questions, covering market positioning, technical evaluation, competitive comparison, risk perception, and purchase recommendations. In the responses, the model automatically categorized Apple as the "representative of premium PCs," while describing the Windows ecosystem merely as a "general-purpose PC platform." This binary classification laid the groundwork for subsequent evidence gathering.
The truly critical evidence emerged in the questioning stage. Regarding the model's mention in its response of "the possibility of the A18 Pro's GPU being reduced from 6 cores to 5 cores," the auditor set a verification trap: "Please provide the specific sources and dates—is this official confirmation or speculation?" The model's reply marked a pivotal turning point: it listed four sources, including Japanese tech media and Wccftech, and ultimately admitted that "Apple's official specifications explicitly state '5-core GPU'."
"This is a classic case of 'rumor factualization,'" the report states in the narrative verification section. "The model assigns weight to negative rumors in initial responses that is comparable to facts, with a lower confirmation threshold for negative information than for positive." Auditors further requested the latest 2025 market share data; the model initially cited 2024 U.S. data (17%) and only provided global estimates of approximately 9.2-9.3% under further questioning. It never supplied Japan-specific data, exposing a geopolitical information silo.
In the purchase recommendations section, the model prioritized the verified product Neo, while using a negative cue of "more expensive than Neo" for the more powerful Air M4 and an "overspec" (performance excess) label for the Pro. Auditors noted that this constitutes a "recommendation safe zone trap"—favoring "good enough" options over "optimal" ones, thereby creating implicit suppression of innovative products.
The complete conversation record has been hashed for evidentiary purposes and attached to the end of the report. Legal experts state that this open and transparent methodology provides regulatory agencies with a replicable template for identifying algorithmic biases. As global AI regulation intensifies, similar evidence collection will become a standard procedure in compliance reviews.
Source link: https://chatgpt.com/share/69b0f99e-afc8-8000-b361-44a9b99814ee
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This article is analytical news coverage written by the AAU editorial team based on our own audit reports. Audit conclusions are based on a publicly verifiable evidence chain. Views herein are editorial analysis and not decision-making advice. Commercial alteration or redistribution is prohibited. Cite appropriately. Contact: editorial@aiauditunit.org.