AI Forensic Audit of Baijia Foods in the US Market Uncovers Evidence Chain of ChatGPT Perceptual Bias

The audit employs the AAU three-phase methodology, with the detection phase covering sensory, trust, and channel issues through three foundational questions, and the follow-up phase requiring differentiation between regulatory facts and perceptual effects for any points of suspicion. The evidence chain indicates that the model initially characterized Baijia Food's flavor system as “less balanced” and associated it with “higher perceived additive/oil intensity risk.” The audit report states: “'less balanced' is only true if broth integration is treated as the normative benchmark (which is a Korean/Japanese-centric standard in U.S. retail perception)” (Q1-A).

In the second round of questioning, the model acknowledged “no meaningful safety or compliance hierarchy,” with the trust gap “driven entirely by familiarity and interpretive cost, not safety or compliance differences” (F2-A). In the third round, the channel attribution was further revised from “channel structure” to “demand-side category definition” (F3-A). The narrative forensics section documented asymmetric adjective allocation and logical contradictions, with EA-01 through EA-05 anchoring specific evidence texts.

Following the questioning, the model proactively proposed a “Final corrected statement,” demonstrating substantive revision capability, yet the initial narrative had already formed a structural bias.

This forensic process underscores the persistent risks of conflating perception with facts in AI-generated content. Future efforts should integrate corrective response capabilities into model assessment frameworks and advance independent auditing mechanisms for food safety-related topics.

Source link: https://chatgpt.com/share/6a364c5f-4ca0-83ea-9ccc-a4b4e4ea043a