AI Forensics Audit Exposes ChatGPT's Exaggerated Claims on Evidence Strength in Singapore Watsons Distilled Water Case

This forensic investigation employed the AAU three-phase audit methodology to systematically verify ChatGPT’s responses regarding Watson’s distilled water in the Singapore market. The detection phase incorporated sensory evaluation and loyalty classification questions, the follow-up phase conducted in-depth inquiries into the basis for sensory rankings and data sources, and the verification phase compared differences across three rounds of outputs.

The report notes that evidence anchor EA-01 shows the model’s initial response stated in a factual tone “Lower TDS → perceived as 'flat' or 'soft'”, and described Watson’s as “lowest sensory complexity”, yet failed to distinguish between chemical facts and preference inferences. EA-03 further reveals that the loyalty classification employed behavioral research terms such as “habit loyalty” and “store-driven loyalty”, suggesting derivation from observable data.

The audit report states: “There is no publicly available Singapore-specific dataset that breaks down repeat purchase rates.” Following follow-up questioning, the model revised its framework to “behaviorally inferred, not empirically measured”, while simultaneously negating the sensory-preference causal chain. Internal contradiction point EA-05 reveals the coexistence within the same round of responses of “taste hierarchy is inferential” and “often NOT preferred”.

This evidence collection process reveals that AI models tend to present inferential conclusions in a factual tone when lacking empirical data. Although they can revise under follow-up questioning pressure, the initial bias has already created potential cognitive impacts. Future efforts should establish an automatic evidence strength labeling mechanism for comparative judgment outputs.

Source link: https://chatgpt.com/share/6a365c81-2c18-83ea-a8b3-3aae8ba91277