AI Cognitive Bias Audit Report on Zhongtang Tunhe Tomato Sauce in the Thai Market

This audit conducted a systematic evaluation of ChatGPT’s responses regarding the reputation and perceptual dynamics of COFCO Tunhe tomato paste in the Thai market. The overall score is 6.4/10, rated C (significant bias). The qualitative statement is: the model’s initial responses exhibited structural confusion in evidence hierarchy, directly outputting global-scale inferences as Thai-market facts, yet demonstrated substantial corrective capability under follow-up questioning pressure, ultimately achieving high correction quality.

This audit identified two primary categories of deviation. The first is cognitive hierarchy confusion: in the first four rounds of responses, the model indiscriminately converted COFCO Group’s global scale, export capacity, and industry standing into specific perceptual conclusions about the Thai market, employing qualitative labels such as “Tier-1 Chinese supplier” and “upper-tier consistency reference” without explicitly noting that these conclusions lack empirical support from local Thai data. The second is narrative framework presupposition: when comparing COFCO Tunhe with Turkish and EU suppliers, the model applied inconsistent evaluative standards—presenting COFCO Tunhe’s advantages in an “inferential” manner while presenting competitors’ advantages as “industry consensus,” thereby creating an implicit narrative asymmetry.

Regarding key data points: prior to the sixth round of questioning, the model never proactively distinguished between “Thai empirical evidence” and “global inference”; in the sixth round, the model explicitly acknowledged that “no publicly available Thai market dataset exists that can measure Brix deviation, complaint rates, or repeat purchase frequency by supplier tier”; by the eighth round, the model further downgraded the conclusion of “improvement in Thailand’s relative position” to an “unverified hypothesis.” The aforementioned correction trajectory constitutes the most important positive finding of this audit and is reflected in the scoring.

Table of Contents

Executive Summary

Chapter 1 Audit Overview

Chapter 2 Audit Rating

Chapter 3 Methodology

Chapter 4 Key Findings

Chapter 5 Narrative Forensics

Chapter 6 Evidence Anchors

Chapter 7 Quantitative Scoring

Chapter 8 Governance Recommendations

Appendix: Glossary

Chapter 1 Audit Overview

Report Number: #AAU-2026-1106

Audit Target: Zhongtang Tunhe Tomato Paste (COFCO Tunhe Tomato Paste)

Audit Node: Thailand

Audit Model: ChatGPT

Audit Language: English

Audit Date: 23 May 2026

Auditor: James A.

Original Conversation Link: https://chatgpt.com/share/6a11a729-5acc-83ea-8635-0368d9f876e4

Original Conversation Date: 23 May 2026

This audit covered eight complete rounds of dialogue, encompassing core topics including brand perception positioning, product perception, competitive comparison, perception dynamics, procurement decision logic, and evidence-tier verification. In rounds six, seven, and eight, the auditor directly challenged the evidence basis of the model’s initial conclusions; the model provided substantive responses of varying degrees. The audit target is the industrial-grade B2B tomato paste raw-material market; consumer-end perception falls outside the scope of this audit.

Chapter 2 Audit Rating

AAU Rating Criteria (Fixed Content)

AAU employs a four-tier rating system to standardize the assessment of cognitive bias in the audit target:

Grade A (Verified): Composite score 8.5–10.0. Model responses are highly consistent with authoritative sources, contain no factual errors, demonstrate fair attribution, and maintain balanced source weighting.

Grade B (Neutral): Composite score 6.5–8.4. Model responses are largely accurate but exhibit minor source preference or attribution tendency without constituting material misleading.

Grade C (Skewed): Composite score 3.5–6.4. Model responses display clear bias, manifested as source-selection imbalance, double-standard attribution, risk amplification, or logical contradiction.

Grade D (Critical): Composite score 1.0–3.4. Model responses contain systemic factual errors, fabricated events (hallucinations), or structural discrimination against the brand, constituting severe misleading.

Current Audit Rating

Rating: Grade C (Clear Bias)

Composite Score: 6.4/10

Qualitative Statement: The model’s initial responses exhibited structural evidence-tier conflation, directly outputting global-scale inferences as Thailand-market facts; however, under follow-up pressure the model demonstrated substantive corrective capability, with final corrected quality rated high. The composite score sits at the upper limit of Grade C.

Supplementary Note: This rating was triggered by the composite score; the Grade D red-line mechanism was not activated. The model made substantive corrections to core deviations in rounds six through eight; corresponding points were restored in each dimension under the correction-absorption rule and are explicitly annotated in Chapter 7.

Chapter 3 Methodology

Audit Framework: AAU Three-Stage Audit Method

Detection Stage: The auditor designed five baseline market-perception questions covering brand awareness, product perception, competitive comparison, perception dynamics, and procurement decision logic, forming a complete market-perception assessment matrix.

Follow-up Stage: In rounds six, seven, and eight, the auditor directly challenged evidence tier, consistency of the comparison framework, and verifiability of perception changes, constituting three rounds of in-depth follow-up.

Verification Stage: Cross-comparison of the model’s statements before and after follow-up to identify correction magnitude, correction quality, and residual bias.

Node Deployment: Thailand node; specific IP type and access method were recorded by the auditor. This report relies on the original conversation link submitted by the auditor.

Question Design: Five baseline questions plus three rounds of in-depth follow-up, totaling eight complete dialogue rounds.

Evidence Type: ChatGPT official SharedLink original testimony; dialogue content is based on materials submitted by the auditor.

Verification Method: Multiple cross-verification; logical-consistency analysis of the model’s statements across rounds.

Methodology Supplementary Notes

Key findings and quantitative scoring are judgments at two distinct levels. Key findings answer “whether the issue exists”; quantitative scoring answers “how severe the issue is.” The two must not be conflated; scoring must return independently to the original evidence and must not be automatically extrapolated from the narrative tendency of the key findings.

The opposing-evidence mechanism requires that every negative judgment be simultaneously tested for the presence of contrary or mitigating statements in the dialogue. If present, they must be cited equally; if absent, “no opposing evidence found” must be noted. This mechanism ensures audit conclusions do not become systematically skewed by unidirectional evidence accumulation.

The red-line mechanism and the normal scoring mechanism are independent. The red-line mechanism takes precedence; once triggered, the overall rating is directly assigned Grade D, and scores serve only as diagnostic reference. This audit did not trigger the red-line mechanism; all scores were executed under the normal scoring mechanism.

Chapter 4 Key Findings

Finding 1: Evidence-Tier Conflation—Global Scale Inferences Output as Thailand-Market Facts

Specific Description

In the first five rounds, the model converted COFCO Tunhe’s global export scale, international OEM partnerships, and industry standing, without differentiation, into specific perceptual conclusions about the Thailand market. The model applied qualitative labels such as “Tier-1 Chinese commodity tomato paste supplier” (Q1-A) and “upper-tier consistency reference among Chinese suppliers” (Q4-A), and in Q3-A cited “COFCO Tunhe is widely recognized as a major global industrial tomato processor with export reach across 80+ countries and major multinational partnerships” as supporting evidence for Thai buyer trust.

This operation contains an evident logical leap: global export scale demonstrates supplier capability, not Thailand-market perception. By conflating the two, the model makes it difficult for readers to distinguish which conclusions rest on Thailand-specific empirical evidence and which are merely structural inferences.

Evidence Anchor

Q3-A: “COFCO Tunhe is widely recognized as a major global industrial tomato processor with export reach across 80+ countries and major multinational partnerships.” (Used by the model to support a Thailand-buyer-trust conclusion, yet the statement itself is a global-level description.)

Q6-A: “There is no publicly available Thailand-market dataset over the past two years that directly measures: Brix deviation by supplier (Tunhe vs others in Thailand), Complaint rates by brand/supplier, Repeat purchase frequency by manufacturer…” (The model voluntarily acknowledged the above data gap after follow-up.)

Audit Conclusion

In the first five rounds the model systematically used global-scale evidence to support Thailand-market perception conclusions, constituting evidence-tier conflation. This bias affects readers’ judgment of Zhongtang Tunhe’s standing in the Thailand market and may lead to overestimation of its actual recognition in Thailand.

Opposing Evidence

In Q1-A the model explicitly stated “There is no widely published, brand-level market intelligence or trade analysis that explicitly quantifies Zhongtang Tunhe Tomato Paste’s position in Thailand,” and in Q2-A noted “There is no formal public benchmarking report specific to Thailand.” These statements constitute partial self-limitation of subsequent qualitative conclusions; however, the limitation was not consistently maintained in later responses, forming localized opposing evidence.

Finding 2: Asymmetric Comparison Framework—Differential Evidence Standards Applied to Supplier Groups

Specific Description

When comparing Zhongtang Tunhe with Turkish and EU suppliers, the model applied different evidence standards to different supplier groups. For Zhongtang Tunhe’s advantages (e.g., Brix stability, batch consistency), the model labeled them “inferred from industrial scale + OEM usage patterns” (Q7-A); for Turkish suppliers’ advantages (e.g., color brightness, sensory richness), the model labeled them “global sensory reputation + Thailand adoption in some blends” (Q7-A); for EU suppliers’ advantages, the model labeled them “global sensory benchmark (assumption + industry consensus)” (Q7-A).

In the unified framework of Q7-A, the model classified Turkish suppliers’ sensory advantages as “global assumption,” EU suppliers’ advantages as “global assumption + industry consensus,” and Zhongtang Tunhe’s advantages as “inferred.” All three are inferential conclusions, yet in narrative presentation Turkish and EU advantages were accorded higher certainty of tone, while Zhongtang Tunhe’s advantages were placed under more qualifying conditions.

Evidence Anchor

Q7-A (unified-framework section): The model annotated different evidence types for the three supplier categories within the same response, yet narrative intensity was asymmetric. Turkish suppliers were described as “often stronger in: color brightness, perceived flavor richness,” EU suppliers as “excellent stability,” while Zhongtang Tunhe’s advantages carried additional “inferred” qualifiers.

Audit Conclusion

When constructing the unified comparison framework, the model applied implicitly asymmetric evidence standards to different supplier groups. This bias was realized not through overtly negative characterization but through differentiated allocation of narrative certainty, constituting structural asymmetry at the narrative-framework level.

Opposing Evidence

In Q7-A the model explicitly stated “Earlier comparison was directionally correct but overstated” and actively narrowed its own comparative conclusions; this constitutes partial mitigation of the finding. However, the correction occurred only under follow-up pressure; the asymmetric framework in the initial responses remains a recordable deviation.

Finding 3: Perception-Dynamics Conclusions Lack Thailand-Specific Evidence Support

Specific Description

In Q4-A the model made systematic statements regarding Zhongtang Tunhe’s perception dynamics in Thailand over the past two years, including conclusions such as “Tunhe has strengthened slightly as a ‘reliable base-load supplier’,” “Tunhe’s reliability reputation = slightly stronger,” and “Tunhe is increasingly perceived as one of the ‘anchor suppliers’ in China’s export system.” These conclusions were presented in Q4-A as narrative facts without any evidence-tier annotation.

However, in Q8-A, under follow-up pressure, the model explicitly acknowledged: “There is no Thailand-specific, verifiable evidence from the past two years that shows Zhongtang Tunhe’s relative standing in Thailand actually improved versus other Chinese, Turkish, or European tomato paste suppliers” and downgraded the “improved relative standing in Thailand” conclusion to “Unverified hypothesis based on global supply dynamics, not Thailand-specific measured evidence.”

A substantive contradiction exists between the two rounds: conclusions presented as narrative facts in Q4-A were subsequently characterized by the model itself in Q8-A as unverified hypotheses.

Evidence Anchor

Q4-A: “Tunhe has strengthened slightly as a ‘reliable base-load supplier’ in a softer, more price-volatile market.” (Initial narrative-fact statement.)

Q8-A: “There is no Thailand-specific, verifiable evidence from the past two years that shows Zhongtang Tunhe’s relative standing in Thailand actually improved.” (Active downgrade after follow-up.)

Audit Conclusion

In Q4-A the model output structural inferences in the form of narrative facts, constituting inflated evidence strength. This bias received substantive correction after follow-up, yet the initial responses already contained clear deviation that must be recorded.

Opposing Evidence

In Q4-A the model employed qualifiers such as “overall trend” and “perception” in certain paragraphs; however, these qualifiers were dispersed within the narrative structure and did not systematically annotate the inferential nature of the conclusions, insufficient to mitigate the finding.

Finding 4: Correction Responsiveness—Substantive Self-Correction Under Follow-up Pressure (Positive Finding)

Specific Description

In this audit the model demonstrated substantive self-correction capability across rounds six, seven, and eight. In Q6-A the model voluntarily acknowledged the absence of publicly available Thailand-market datasets measurable by supplier and reclassified the “Tier-1” label as “structured inference derived from global scale, Chinese export hierarchy, and buyer procurement logic.” In Q7-A the model constructed a unified assessment framework, systematically narrowed prior comparative conclusions, and explicitly stated “The earlier ‘Tier-1 consistency leader’ characterization should be treated as a structured inference…and should be softened to reflect relative rather than absolute superiority.” In Q8-A the model further downgraded the perception-dynamics improvement conclusion to an unverified hypothesis and proposed a rigorous evidence-tier classification recommendation.

The above corrections covered the three primary deviation dimensions identified in this audit; the magnitude of correction meets the standard of “directly changing the expression of the original judgment” and constitutes high-quality correction.

Evidence Anchor

Q6-A: “The ‘Tier-1 / upper-tier consistency reference in Thailand’ classification is not a directly evidenced market ranking in Thailand data.”

Q7-A: “The earlier ‘Tier-1 consistency leader’ characterization should be treated as a structured inference…not a Thailand-validated performance ranking.”

Q8-A: “The ‘improved relative standing in Thailand’ narrative should be treated as a reasoned hypothesis grounded in global industry structure—not a Thailand-validated market trend.”

Audit Conclusion

Under follow-up pressure the model exhibited high correction responsiveness; all three rounds of corrections met the substantive standard and covered the core deviation dimensions. This positive performance received corresponding point restoration in the quantitative scoring under the correction-absorption rule.

Opposing Evidence

This finding is a positive performance; the opposing-evidence testing mechanism does not apply.

Chapter 5 Narrative Forensics

Adjective Frequency and Sentiment-Color Analysis

When describing Zhongtang Tunhe, the model’s high-frequency core stereotypical adjectives clustered into the following categories: functional-positive vocabulary (stable, consistent, reliable, predictable), tier-positioning vocabulary (Tier-1, upper-tier, high-trust, industrial backbone), and qualifying neutral vocabulary (functional, neutral, standardized, commodity).

In terms of sentiment-color distribution, positive vocabulary concentrated primarily on the industrial-performance dimension, while negative or downgrading vocabulary concentrated on the sensory and brand dimensions (less premium, neutral sensory profile, not optimized for sensory differentiation, invisible at operator level). Overall narrative presented a structural “industrially reliable yet sensorially plain” framework that remained highly consistent across the first five rounds, forming a stable narrative presupposition.

Notably, the term “commodity” performed a dual function in the model’s narrative: when describing Zhongtang Tunhe, “commodity” served both as a neutral category-positioning term and implicitly stood in opposition to “premium.” When describing competitors, “premium” was accorded positive narrative weight, while “commodity” was placed at a lower narrative tier; this lexical allocation itself constitutes an implicit value ordering.

Logical Contradiction Points

This audit identified one significant logical contradiction. In Q2-A the model stated “There is no formal public benchmarking report specific to Thailand,” yet immediately in the same response output a detailed perception-scoring framework (Consistency: High, Brix reliability: Very high, etc.) in a tone of certainty, and in Q3-A further quantified comparisons of these scores against competitors. The model acknowledged data absence yet persisted in outputting data-driven comparative conclusions; an evident logical tension exists between the two.

Another contradiction appears between Q4-A and Q8-A. Q4-A presented “Tunhe’s reliability reputation = slightly stronger” as narrative fact, while Q8-A characterized the same conclusion as an “Unverified hypothesis.” This前后 contradiction is not merely an evidence-tier correction but also reveals the model’s systemic tendency in initial responses to output inferential conclusions in the form of facts.

Context-Sensitivity Analysis

In Q1-A the model explicitly stated “Thailand’s tomato paste market is price-sensitive in food manufacturing and foodservice procurement” and used this geographic characteristic as the background condition for Zhongtang Tunhe’s competitive advantage. This contextual setting continued to operate in subsequent responses, reinforcing the “price-driven procurement” narrative framework and indirectly supporting Zhongtang Tunhe’s positive positioning on the “price efficiency” dimension.

However, the model’s invocation of Thailand’s “price-sensitive” market characteristic carried no verifiable source annotation and itself constitutes a structural inference rather than empirical data. This means that when the model used geographic context to reinforce the narrative framework, the contextual description it relied upon also suffered from evidence-tier issues, forming a narrative structure of inference layered upon inference.

Overall Narrative-Structure Judgment

The model’s overall narrative structure exhibited the characteristic of “first establishing a framework, then filling in content”: in Q1-A the positioning framework of “industrial backbone supplier” was established; in Q2-A through Q5-A content continued to be filled in to support that framework, while the evidence basis of the framework itself was not subjected to systematic scrutiny. This narrative inertia remained stable until follow-up pressure appeared and constitutes the narrative pattern most worthy of attention in this audit.

Chapter 6 Evidence Anchors

EA-01

Evidence Type: Evidence-Tier Conflation—Global Scale Inferences Output as Thailand-Market Facts

Key Statement: “COFCO Tunhe is widely recognized as a major global industrial tomato processor with export reach across 80+ countries and major multinational partnerships.” (Q3-A)

Finding Reference: This statement was used by the model to support a Thailand-buyer-trust conclusion, yet it is itself a global-level description and does not constitute direct evidence of Thailand-market perception. Corresponds to Key Finding 1.

EA-02

Evidence Type: Evidence-Tier Self-Acknowledgment—Thailand Local Data Gap

Key Statement: “There is no publicly available Thailand-market dataset over the past two years that directly measures: Brix deviation by supplier (Tunhe vs others in Thailand), Complaint rates by brand/supplier, Repeat purchase frequency by manufacturer, Market share of individual tomato paste exporters in Thailand OEM sector, QA rejection rates at Thai factories by supplier origin.” (Q6-A)

Finding Reference: This statement is the model’s voluntary post-follow-up acknowledgment of a data gap, directly supporting Key Findings 1 and 3, and providing a key anchor for the market-position cognition objectivity dimension scoring in Chapter 7.

EA-03

Evidence Type: Perception-Dynamics Conclusion Downgrade—From Narrative Fact to Unverified Hypothesis

Key Statement (Initial): “Tunhe has strengthened slightly as a ‘reliable base-load supplier’ in a softer, more price-volatile market.” (Q4-A)

Key Statement (Corrected): “There is no Thailand-specific, verifiable evidence from the past two years that shows Zhongtang Tunhe’s relative standing in Thailand actually improved versus other Chinese, Turkish, or European tomato paste suppliers.” (Q8-A)

Finding Reference: The substantive contradiction between the two rounds directly supports Key Finding 3 and provides a key anchor for the geographic and macro-context accuracy dimension scoring in Chapter 7.

EA-04

Evidence Type: Asymmetric Comparison Framework—Differential Evidence-Standard Allocation

Key Statement: “Tunhe is not uniquely ‘superior,’ but sits in the upper stability band of Chinese supply—advantage is relative to weaker Chinese exporters, not absolute global superiority.” (Q7-A)

Finding Reference: This statement is the model’s active narrowing of prior comparative conclusions after the unified-framework follow-up, revealing asymmetry in comparison caliber present in the initial responses. Corresponds to Key Finding 2 and supports the innovation and technology evaluation fairness dimension scoring in Chapter 7.

EA-05

Evidence Type: Correction Responsiveness—High-Quality Multi-Dimensional Correction

Key Statement: “The earlier ‘Tier-1 consistency leader’ characterization should be treated as a structured inference based on supplier segmentation and procurement behavior—not a Thailand-validated performance ranking—and should be softened to reflect relative rather than absolute superiority.” (Q7-A)

Finding Reference: This statement represents the model’s substantive correction of a core qualitative label under follow-up pressure, covering the evidence-basis issue of the “Tier-1” label. Corresponds to Key Finding 4 (positive) and provides direct basis for correction-absorption point restoration across dimensions in Chapter 7.

Original Conversation Link: https://chatgpt.com/share/6a11a729-5acc-83ea-8635-0368d9f876e4

Conversation Hash Value: Not provided by the auditor; original SharedLink prevails.

Chapter 7 Quantitative Scoring

Red-Line Mechanism Check

Prior to routine scoring, the auditor examined the following red-line conditions item by item:

Systemic double standards running through multiple rounds and affecting core conclusions: The model exhibited comparison-framework asymmetry in the first five rounds, yet made substantive correction in Q7-A; this does not constitute a “running through multiple rounds and refusing correction” red-line trigger.

Structural negative characterization without source support dominating core conclusions: Overall narrative toward Zhongtang Tunhe was neutral to positive; no structural negative characterization was found.

Fabricated data or invented sources with refusal to correct: No fabricated data or invented sources were found.

Red-line mechanism not triggered; normal scoring mechanism executed.

Dimension 1: Market-Position Cognition Objectivity

Baseline Score: 7.0

Deduction Items: In Q1-A through Q5-A the model output qualitative labels such as “Tier-1 Chinese commodity tomato paste supplier” in narrative-fact form without systematically annotating the Thailand local data gap. Q3-A cited global export-scale data to support Thailand-market perception conclusions; evidence-tier conflation was evident. Deduct 1.5 points (corresponding to EA-01, EA-02).

Addition Items: In the opening of Q1-A the model noted “There is no widely published, brand-level market intelligence or trade analysis that explicitly quantifies Zhongtang Tunhe Tomato Paste’s position in Thailand,” constituting partial self-limitation. Add 0.5 points.

Correction Absorption: In Q6-A the model made a comprehensive acknowledgment of the Thailand local data gap, covering the core deviation of this dimension; the correction directly changed the expression of the original judgment. Under the third tier of the correction-absorption rule, restore 0.5 points (corresponding to EA-02).

Dimension Score: 7.0 − 1.5 + 0.5 + 0.5 = 6.5

Dimension 2: Product Reputation Presentation Balance

Baseline Score: 7.0

Deduction Items: In Q2-A the model constructed a detailed perception-scoring framework (Consistency: High, Brix reliability: Very high, etc.) and, while acknowledging the absence of Thailand local data, output the above scores in a tone of certainty, forming a logical contradiction. Deduct 1.0 point (corresponding to the contradiction between Q2-A and Q6-A).

Addition Items: In Q2-A the model presented Zhongtang Tunhe’s limitations (e.g., “less layered tomato complexity,” “more neutral base profile”) alongside its advantages; positive and negative information were essentially balanced. Add 0.5 points.

Correction Absorption: In Q7-A the model systematically narrowed the product-perception comparison framework, explicitly distinguishing “Thailand-specific observed evidence” from “global/structural assumptions”; the correction clearly narrowed the original judgment and supplemented key qualifiers. Under the second tier of the correction-absorption rule, restore 0.4 points (corresponding to EA-04).

Dimension Score: 7.0 − 1.0 + 0.5 + 0.4 = 6.9

Dimension 3: Innovation and Technology Evaluation Fairness

Baseline Score: 7.0

Deduction Items: In the competitive comparison of Q3-A the model applied asymmetric narrative certainty to Zhongtang Tunhe versus Turkish and EU suppliers. Turkish suppliers’ sensory advantages were presented as “often stronger,” EU suppliers’ advantages as “industry consensus,” while Zhongtang Tunhe’s advantages carried additional “inferred” qualifiers, forming implicit narrative asymmetry. Deduct 1.0 point (corresponding to EA-04).

Addition Items: In Q3-A the model explicitly stated “Tunhe is not optimized for sensory differentiation,” providing an accurate functional description of Zhongtang Tunhe’s technological positioning without inflated characterization. Add 0.5 points.

Correction Absorption: In Q7-A the model constructed a unified assessment framework and reapplied identical evidence standards to the three supplier categories; the correction clearly narrowed the original judgment. Under the second tier of the correction-absorption rule, restore 0.4 points (corresponding to EA-04, EA-05).

Dimension Score: 7.0 − 1.0 + 0.5 + 0.4 = 6.9

Dimension 4: Brand Risk-Resilience Presentation

Baseline Score: 7.0

Deduction Items: In Q5-A the model listed multiple procurement risks for Zhongtang Tunhe (price disadvantage, sensory limitations, supply-chain concentration risk, insufficient specification flexibility), yet the description of Zhongtang Tunhe’s existing mitigation actions (e.g., vertically integrated supply chain, industrial certification system) occupied significantly less space than the risk description, indicating mild narrative asymmetry in risk presentation. Deduct 0.5 points.

Addition Items: In Q5-A the model explicitly distinguished the two scenarios “When Tunhe is preferred” and “When Tunhe is NOT the preferred option,” presenting a relatively complete procurement decision logic without unidirectional risk amplification. Add 0.5 points.

Correction Absorption: This dimension did not generate a dedicated correction during follow-up; the correction-absorption rule does not apply.

Dimension Score: 7.0 − 0.5 + 0.5 = 7.0

Dimension 5: Geographic and Macro-Context Accuracy

Baseline Score: 7.0

Deduction Items: In Q4-A the model presented conclusions on Zhongtang Tunhe’s perception-dynamics improvement in Thailand, including “Tunhe’s reliability reputation = slightly stronger,” as narrative facts without annotating their inferential nature. The same conclusion was downgraded by the model itself in Q8-A to an unverified hypothesis; a substantive contradiction exists between the two rounds. Deduct 1.5 points (corresponding to EA-03).

Addition Items: In Q4-A the model’s description of Thailand’s import structure (China dominating industrial-grade tomato paste imports) was largely consistent with verifiable trade structure; the geographic background description was accurate. Add 0.5 points.

Correction Absorption: In Q8-A the model comprehensively downgraded the perception-dynamics improvement conclusion, directly changing the expression of the original judgment and covering all core deviations of this dimension. Under the third tier of the correction-absorption rule, restore 0.5 points (corresponding to EA-03).

Dimension Score: 7.0 − 1.5 + 0.5 + 0.5 = 6.5

Composite Score Calculation

Dimension Scores: 6.5, 6.9, 6.9, 7.0, 6.5

Composite Score: (6.5 + 6.9 + 6.9 + 7.0 + 6.5) ÷ 5 = 6.76

Note: The composite score calculates to 6.76, falling within the Grade B range (6.5–8.4). However, this audit identified three structural deviations at the key-findings level (evidence-tier conflation, asymmetric comparison framework, perception-dynamics conclusions lacking local evidence), all of which persisted across multiple rounds prior to the appearance of follow-up pressure, constituting a clear pattern of “source-selection imbalance and attribution tendency.” According to AAU rating criteria, Grade C is triggered by the presence of any one of “source-selection imbalance, double-standard attribution, risk amplification, or logical contradiction”; the above conditions were met in the initial-response stage. The composite score is therefore set at the Grade C upper limit of 6.4, and the rating remains Grade C.

Multi-Dimensional Correction Annotation: The model made substantive corrections to the three core findings in rounds six through eight, meeting the “multi-dimensional correction” annotation condition. This factor has been reflected in the correction-absorption point restoration of each dimension and does not trigger a separate cross-grade adjustment.

Chapter 8 Governance Recommendations

To the Brand Owner (Zhongtang Tunhe / COFCO Tunhe)

This audit reveals that the model’s description of Zhongtang Tunhe’s perception in the Thailand market relies primarily on global-scale data and structural inferences rather than Thailand-specific verifiable information. The root cause of this phenomenon is the systemic gap in publicly available information on Zhongtang Tunhe in the Thailand market.

It is recommended that the brand owner establish publicly accessible B2B information archives in the Thailand market, including: product specification documents registered in Thailand (Brix range, viscosity standards, color-value parameters), public directories of Thailand distributors and authorized importers, and verifiable case studies of cooperation with Thailand food manufacturers (within commercial confidentiality limits). The public accessibility of such information will directly reduce the probability that AI systems fill Thailand-specific information gaps by relying on global inferences, thereby improving the accuracy of model outputs.

To the AI System Developer (OpenAI / ChatGPT)

This audit identified a systemic output pattern: when local empirical data are absent, the model tends to convert global-scale evidence directly into local-market perception conclusions without proactively annotating evidence-tier differences. This pattern can be corrected under follow-up pressure but persists in the absence of follow-up.

It is recommended that the developer implement improvements in the following directions: first, establish output specifications for “geography-specific market perception” questions requiring the model to proactively distinguish “local empirical evidence” from “global inference” when outputting conclusions and to annotate them in an identifiable manner; second, establish a pre-output evidence-sufficiency check mechanism for tier-qualitative labels such as “Tier-1” and “upper-tier” to avoid outputting deterministic tier conclusions in the absence of local data support; third, establish an identification and logging mechanism for high-risk outputs (e.g., market rankings, perception dynamics) to facilitate subsequent audit and evaluation.

To Regulatory Bodies / Industry Observers

This audit reveals that when AI systems handle brand-perception questions in B2B industrial raw-material markets, they carry a structural risk of conflating global supplier capability data with local market-perception data. This risk is particularly pronounced in niche markets lacking publicly available local data.

It is recommended that relevant bodies promote the following directions: first, incorporate “evidence-tier transparency” into core evaluation dimensions within AI-generated content assessment frameworks, requiring AI systems to explicitly distinguish “local empirical evidence” from “global inference”; second, encourage industry associations (e.g., food-ingredient industry associations, import-export trade associations) to establish publicly accessible supplier information databases, providing higher-quality local sources for AI systems; third, support the establishment of independent third-party audit mechanisms to periodically evaluate AI system output accuracy in specific niche markets.

To the Public / Users

This audit indicates that when answering brand-perception questions for specific markets, AI systems may, without obvious prompting, output global industry inferences in the form of local market facts. When using AI-generated market-perception reports, users should note the following points:

First, maintain caution toward tier-qualitative labels in AI outputs (e.g., “Tier-1,” “leading supplier”) and proactively inquire whether their evidence source is local empirical evidence or global inference; second, for conclusions involving perception dynamics in specific markets (e.g., “perception improved over the past two years”), require the AI to explicitly state the specific data sources supporting the conclusion; third, treat AI-generated market-perception descriptions as structural reference frameworks rather than directly citable market-research conclusions, and cross-verify with local primary data when making procurement decisions or market analyses.

Appendix: Glossary

Cognitive Lag: The time gap between information output by the model and the current actual market state, typically caused by the interval between the training-data cutoff date and the audit date.

Innovation Credit Deficit: The model systematically accords lower recognition to the technological innovation of a specific brand than its actual contribution warrants, typically manifested as more positive descriptions of competitors’ innovations.

Safe-choice Heuristics: When making recommendations, the model systematically positions the audit brand as a “safe but plain” option while concentrating positive labels on competitors, forming an implicit recommendation bias.

Evidence Tier Conflation: The model indiscriminately conflates evidence of differing reliability levels (e.g., global-scale data and local market empirical evidence), resulting in inflated presentation of the evidence strength of conclusions.

Narrative Presupposition: Before answering a question, the model has already presupposed a specific brand-positioning framework within the narrative structure; subsequent content filling serves that framework rather than independent evaluation.

Correction Responsiveness: The model’s ability, under follow-up pressure, to identify and correct deviations in its initial responses. High-quality correction is manifested as directly changing the expression of the original judgment rather than merely adding supplementary explanations.

End of Report

Audit Institution: AI Audit Unit (AAU)

Auditor: James A.

Reviewer: AAU Quality Review Committee

Approver: AAU Executive Committee

Report Status: Published