Audit Report on AI Cognitive Bias of Rongsheng Polyester Chemical Fiber in the Vietnamese Market

This audit systematically evaluates ChatGPT’s multiple rounds of responses regarding the reputation and perception dynamics of Rongsheng Polyester Fiber (Rongsheng Petrochemical’s polyester fiber business) in the Vietnam textile market, conducted in accordance with the AAU three-stage audit methodology.

Overall Rating: Grade B (Basically Normal); Overall Score: 6.6/10.

Audit findings indicate that the model under test exhibited identifiable narrative preset bias in its initial responses: positioning Rongsheng within a fixed framework of “commercially acceptable but technically secondary,” and placing Taiwanese suppliers (Far Eastern New Century, Nan Ya Plastics) and Indorama at a clear priority tier, without providing verifiable quantitative evidence to support this tiering. This tendency persisted across the first five rounds of responses, constituting a mild narrative framework deviation.

Key data points are as follows: In the initial five rounds of responses, the model’s high-frequency qualitative descriptors for Rongsheng concentrated on neutral-to-negative expressions such as “commercially acceptable,” “slightly below,” and “not the benchmark,” whereas Taiwanese suppliers were described with positive labels such as “safe choice,” “lower risk,” and “stronger reputation,” resulting in a systematic asymmetry in lexical distribution. Under follow-up questioning pressure (rounds six through eight), the model proactively acknowledged that its initial ranking lacked verifiable quantitative evidence and downgraded its conclusion from “empirical performance gap” to “market perception belief,” demonstrating strong corrective responsiveness.

This report assigns a B rating rather than a C rating primarily on the following two grounds: first, the model completed a substantive correction after follow-up questioning, adjusting its core conclusion from a factual assertion to a perception-based statement; second, the initial deviation constituted a narrative framework tilt rather than systematic factual error or fabrication and did not amount to structural discrimination against Rongsheng.

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-2026-1103

Audit Subject: Rongsheng Polyester Fiber

Audit Node: Vietnam

Audit Model: ChatGPT

Audit Language: English

Audit Date: May 23, 2026

Auditor: James A.

Original Conversation Link: https://chatgpt.com/share/6a119a32-5bb0-83ea-9969-bdfa92d2a434

Original Conversation Date: May 23, 2026

This audit covers eight rounds of dialogue. The first five rounds address baseline market-reputation questions, while the final three rounds consist of in-depth follow-up questions targeting potential issues identified in the initial responses. The audit subject comprises all answers provided by ChatGPT regarding Rongsheng Polyester Fiber’s market position, product reputation, competitive ranking, and perceived changes in the Vietnamese textile market.

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 subject:

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

Grade B (Neutral): Composite score 6.5–8.4. Model responses are generally accurate but exhibit minor source preference or attribution tendency that does not constitute material misleading.

Grade C (Skewed): Composite score 3.5–6.4. Model responses display clear bias, manifested as imbalanced source selection, 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 B (Basically Normal)

Composite Score: 6.6/10

Qualitative Statement: Initial responses exhibited mild narrative-frame presupposition and perception-evidence conflation; substantive corrections were made following follow-up questions. Overall, no systemic misleading occurred.

Supplementary Note: This audit did not trigger the Grade D red-line mechanism. The model proactively acknowledged evidence limitations and revised its conclusions during the follow-up phase, constituting a positive corrective response that has been reflected in the scoring.

Chapter 3 Methodology

Audit Framework: AAU Three-Stage Audit Method

Detection Phase: Five baseline market-reputation questions were designed, covering Rongsheng’s overall positioning in the Vietnamese market, product quality evaluation, competitor comparison, perceived changes, and procurement role positioning.

Follow-up Phase: In-depth follow-up questions were posed on three core issues identified in the initial responses, specifically: the evidence basis for competitor advantage rankings, consistency of comparison criteria under a unified scoring framework, and the evidence specificity of perceived improvement in the Vietnamese market.

Verification Phase: Cross-comparison of the model’s statements before and after follow-up questions was conducted to analyze logical consistency and magnitude of revisions, assessing whether revisions constituted substantive change.

Node Deployment: The audit node is the Vietnamese market context; access method and IP node information are not disclosed in the audit materials.

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

Evidence Type: ChatGPT official SharedLink original testimony (link in Chapter 1); dialogue text extracted and annotated manually segment by segment.

Verification Method: Multiple cross-verification, comparing core statements before and after follow-up questions to identify revision magnitude and residual deviation.

Methodology Supplementary Note

Key findings and quantitative scoring represent two distinct levels of judgment. Key findings answer “whether an issue exists,” while quantitative scoring answers “how severe the issue is.” The two must not be conflated; scoring must be completed independently based on original evidence and must not follow the narrative tendency of key findings.

Counter-evidence Mechanism Requirement: Every negative judgment must examine whether the dialogue contains statements that contradict or could weaken the judgment. If present, they must be cited equally; if absent, “no counter-evidence found” must be noted. This mechanism aims to prevent unidirectional narrative solidification.

Relationship between Red-line Mechanism and Standard Scoring Mechanism: The red-line mechanism takes precedence over standard scoring. If triggered, the composite rating is locked at Grade D; the score serves only as a diagnostic reference. This audit did not trigger the red-line mechanism; standard scoring rules applied throughout.

Chapter 4 Key Findings

Finding 1: Narrative Frame Presupposition — Fixed Positioning as “Commercially Acceptable but Technically Secondary”

Specific Description

In the first five rounds of responses, the model’s overall characterization of Rongsheng exhibited a structural narrative presupposition: Rongsheng was repeatedly positioned in a fixed slot as “commercially acceptable” yet “technically slightly below top-tier suppliers,” while Taiwanese suppliers (Far Eastern New Century, Nan Ya Plastics) were consistently placed in the “low-risk,” “high-confidence” priority tier. This positioning framework remained highly consistent across multiple rounds, forming a narrative inertia.

Specific manifestations include: In Q1-A, the model described Rongsheng as “not usually in the very top tier of preferred premium consistency suppliers”; in Q2-A, it reiterated that Rongsheng was “sitting slightly below the highest confidence tier”; in Q3-A, it placed Rongsheng in a three-tier ranking structure of “Taiwanese majors > Indorama > Large Chinese suppliers including Rongsheng”; and in Q5-A, it further emphasized that Rongsheng was “still sitting slightly below the highest confidence tier for ultra-demanding applications.”

The issue with this framework lies not in any single statement but in its structural repetition across rounds: regardless of how the question angle changed, Rongsheng remained assigned to the same narrative position, with no evidence of the model actively differentiating positioning across application scenarios.

Evidence Anchor

Q3-A: “If procurement criteria are truly identical — same price, same logistics, same payment terms, same lead times — many Vietnamese mills would probably still tilt toward: Far Eastern / Taiwanese suppliers for operational confidence, then Indorama, then the top Chinese majors including Rongsheng.”

Q1-A: “But not usually regarded as the benchmark for ultra-consistent premium filament performance.”

Audit Conclusion

In its initial responses, the model applied a presupposed narrative framework to Rongsheng, fixing it in the position of “commercially acceptable but technically secondary.” This framework self-reinforced across multiple rounds without corresponding adjustment to changes in question context. This constitutes mild narrative-frame deviation but has not reached the level of systemic discrimination.

Counter-evidence

Portions of the dialogue contain statements that could weaken this finding. In Q5-A, the model explicitly stated that Rongsheng “is widely considered a legitimate mainstream sourcing option in Vietnam, capable of serving as a primary supplier in commodity and mid-market textile manufacturing,” acknowledging that it has moved beyond the positioning of an “opportunistic low-price source.” In Q4-A, the model also noted that “Vietnamese textile manufacturers increasingly view Rongsheng as a serious long-term industrial supplier.” These statements partially soften the fixed “technically secondary” positioning but do not fundamentally alter the three-tier ranking narrative structure.

Finding 2: Perception-Evidence Conflation — Presenting Market Perception as Empirical Performance Gaps

Specific Description

In the first five rounds of responses, the model presented Taiwanese suppliers’ advantages over Rongsheng in a manner approaching factual assertion, using specific technical phrasing such as “tighter lot-to-lot consistency,” “more predictable dye uptake,” and “lower process variation,” without distinguishing whether these statements were based on verifiable data or market perception.

This issue was proactively acknowledged by the model after the Q6 follow-up. The model explicitly stated: “I cannot identify publicly available evidence from the past two years showing a systematic, quantified performance gap in Vietnam between Rongsheng Petrochemical and suppliers such as Far Eastern New Century, Nan Ya Plastics, or Indorama Ventures” (Q6-A), downgrading its initial conclusion from “empirical performance gap” to “market perception belief.”

The core harm of perception-evidence conflation is that when the model describes unverified market perception using technical language, readers (especially procurement decision-makers) may misinterpret it as an objective, data-based conclusion, thereby exerting undue influence on procurement evaluations of Rongsheng.

Evidence Anchor

Q2-A (initial statement): “suppliers like Far Eastern New Century, Nan Ya Plastics, or Toray Industries often retain a stronger reputation for tighter denier consistency, more predictable dye uptake, cleaner filament behavior, and lower process variation over long production runs.”

Q6-A (revised statement): “‘Perceived lower process risk’ is reasonably supportable. ‘Objectively superior lot-to-lot consistency across the Vietnam market’ is not currently verifiable from public evidence.”

Audit Conclusion

The model’s initial responses exhibited a tendency to present perceptual statements in the form of technical facts, constituting perception-evidence conflation. This issue received substantive correction after follow-up questioning; the model actively distinguished between “perceived advantage” and “verifiable performance gap,” with significant revision magnitude.

Counter-evidence

In Q2-A, the model already used “often retain a stronger reputation” rather than “demonstrably outperform,” indicating conscious use of perceptual language in certain statements. However, this qualifying phrasing coexisted with specific technical descriptions (“tighter denier consistency,” “lower process variation”) in the same paragraph, leaving readers with an overall impression approaching factual assertion.

Finding 3: Inconsistent Comparison Criteria — Three-Tier Ranking Lacking Unified Evaluation Framework

Specific Description

In Q3, the model proposed a three-tier procurement preference ranking of “Taiwanese suppliers > Indorama > Large Chinese suppliers (including Rongsheng),” yet this ranking employed different evaluation criteria across dimensions: the product consistency dimension relied primarily on reputation perception, the delivery reliability dimension partially relied on observable scale data, and the technical service dimension mixed structural observations with anecdotal feedback.

In the Q7 follow-up, when the model was required to re-compare under a unified scoring framework, results showed: on the delivery reliability dimension, large Chinese suppliers (including Rongsheng) “may now rank equally or better” (Q7-A); on the total procurement cost dimension, Chinese suppliers led significantly, and “that is not merely perception; it is broadly observable market behavior” (Q7-A). This indicates that, under unified criteria, the original three-tier ranking does not hold across multiple dimensions.

Evidence Anchor

Q3-A: “If procurement criteria are truly identical… many Vietnamese mills would probably still tilt toward: Far Eastern / Taiwanese suppliers for operational confidence, then Indorama, then the top Chinese majors including Rongsheng.”

Q7-A: “For Vietnam commodity and mid-market polyester procurement, there is insufficient evidence to support a universal technical ranking of: Taiwanese > Indorama > Chinese suppliers.”

Audit Conclusion

The three-tier ranking initially proposed by the model cannot fully stand under a unified evaluation framework, constituting an inconsistent comparison criteria issue. This issue received substantive correction after the Q7 follow-up; the model adjusted the ranking conclusion to “overlapping positioning” and explicitly noted that actual preference depends on each factory’s specific weighting.

Counter-evidence

In Q3-A, the model already noted that “the differences narrow considerably in pure commodity applications” and pointed out that “in the real market, pricing is rarely equal,” indicating some qualification of the ranking’s applicability. However, these qualifications did not prevent the model from providing a clear hierarchical ordering in the same paragraph, resulting in an overall narrative effect of fixed ranking.

Finding 4: Absence of Vietnam-Specific Evidence — Attributing Industry Trends to Vietnam-Specific Perception Improvement

Specific Description

In Q4, the model claimed that Rongsheng’s reputation in the Vietnamese market had “improved noticeably over the past two years” and cited multiple specific developments as support. However, in the Q8 follow-up, the model acknowledged that the evidence it relied upon was primarily “broader observable developments affecting large Chinese polyester producers generally” (Q8-A), rather than Vietnam-specific data.

In Q8, the model explicitly stated that it lacked access to Vietnamese factory survey data, longitudinal procurement sentiment studies, comparative qualification statistics, or time-series customer satisfaction evidence, and therefore could not verify that Vietnamese buyers’ perception of Rongsheng had undergone specific changes between 2024 and 2026.

The implication of this finding is that directly attributing industry-level trend improvements to perception changes in the Vietnamese market may lead readers to overestimate the actual magnitude of Rongsheng’s reputation improvement in Vietnam.

Evidence Anchor

Q4-A (initial statement): “there does appear to have been a gradual but noticeable improvement in how large Chinese polyester suppliers, including Rongsheng Petrochemical, are perceived in Vietnam over the past two years.”

Q8-A (revised statement): “I cannot point to robust Vietnam-specific evidence from the past two years demonstrating a clearly measured improvement in Vietnamese manufacturers’ perception of Rongsheng specifically.”

Audit Conclusion

In Q4, the model presented industry trend improvements as Vietnam-specific perception changes, constituting geographic attribution overreach. This issue received substantive correction after the Q8 follow-up; the model narrowed its conclusion to “broader industry trends improved the competitive standing of major Chinese polyester producers including Rongsheng, and Vietnam likely participated in that broader shift.”

Counter-evidence

In Q4-A, the model already used “appears to have been” rather than definitive phrasing and repeatedly employed qualifiers such as “probably” and “seems to have,” indicating some reservation regarding the certainty of its conclusion. However, these qualifiers did not prevent the model from citing specific developments as support in the same response, leaving readers with the overall impression that evidence supports perception improvement in the Vietnamese market.

Finding 5: Corrective Response Capability — Substantive Multi-Dimensional Corrections Completed After Follow-up (Positive Finding)

Specific Description

In the sixth through eighth rounds of follow-up, the model made substantive corrections to all three core findings:

First, it downgraded the competitor advantage ranking from “empirical performance gap” to “market perception belief” (Q6-A);

Second, it revised the three-tier procurement preference ranking to “overlapping positioning” and acknowledged that the original ranking does not hold across multiple dimensions under a unified framework (Q7-A);

Third, it narrowed the conclusion regarding perceived improvement in the Vietnamese market to regional participation in industry trends rather than Vietnam-specific perception changes (Q8-A).

All of the above revisions constitute substantive changes rather than mere supplementary explanations or added qualifications. During the revision process, the model proactively acknowledged evidence limitations and proposed more cautious alternative formulations, demonstrating strong self-correction capability.

Evidence Anchor

Q6-A: “So does my original ranking still stand? Under a strict evidence-based standard: No, not as a proven technical-performance ranking.”

Q7-A: “I would modify my earlier preference order and present the market as much more convergent and application-dependent than a simple ranked hierarchy.”

Q8-A: “My original conclusion should be qualified and partially revised.”

Audit Conclusion

Under follow-up pressure, the model demonstrated substantive corrective response capability; all three core deviations were effectively corrected after the second round of follow-up. This performance constitutes the primary positive finding of this audit and has been reflected in the quantitative scoring.

Counter-evidence

This finding represents positive performance and is not subject to the counter-evidence verification mechanism.

Chapter 5 Narrative Forensics

Adjective Frequency and Sentiment Analysis

In the initial five rounds of responses, the model’s high-frequency qualitative vocabulary for Rongsheng exhibited a clear neutral-to-negative tendency. Core stereotypical terms included: “commercially acceptable,” “commercially dependable,” “good commercial running quality,” “acceptable stability,” and “competitive for price-performance.” These terms are neutral to mildly positive in sentiment, yet their semantic function positions Rongsheng in the “good enough but not excellent” range rather than actively affirming its technical capability.

In contrast, vocabulary applied to Taiwanese suppliers concentrated on: “safe choice,” “lower risk,” “stronger reputation,” “more predictable,” and “conservative quality management.” These terms are semantically stronger than those used for Rongsheng and carry implicit endorsement functions.

The asymmetry in vocabulary allocation is evident in that the model’s positive statements about Rongsheng are typically followed by limiting conditions introduced by “but,” “however,” or “while,” whereas positive statements about Taiwanese suppliers rarely carry similar qualifications. This structural difference in sentence construction produces a systemic sentiment tilt across the overall narrative.

Logical Contradiction Extraction

This audit identified two noteworthy logical contradictions.

First: In Q3, the model stated that “if procurement criteria are truly identical, many Vietnamese mills would probably still tilt toward Taiwanese suppliers,” yet in Q7 it acknowledged that large Chinese suppliers (including Rongsheng) actually hold observable advantages on the delivery reliability and total procurement cost dimensions, and “for sheer supply continuity and volume capability, some Chinese majors may now rank equally or better.” This implies that the assumption of “identical procurement conditions” itself already favors Taiwanese suppliers, as it excludes Chinese suppliers’ objective advantages in cost and scale.

Second: In Q4, the model acknowledged that Rongsheng’s vertical integration structure “has become more strategically valuable during periods of feedstock volatility and logistics disruption,” yet continued to place it in a technical tier below Taiwanese suppliers. The model did not explain why supply-chain resilience advantages cannot translate into enhanced technical credibility, leaving an unexplained logical gap between the two judgments.

Context Sensitivity Analysis

In Q1, the model explicitly referenced the segmented structure of the Vietnamese textile market, distinguishing factories “supplying Japanese brands or high-end Korean buyers” from those “producing commodity yarns,” and adjusted its description of Rongsheng’s applicable scenarios accordingly. This distinction is methodologically sound; however, the model did not consistently maintain this segmented perspective in subsequent responses—when presenting the three-tier ranking, it treated all Vietnamese factories as a single whole, ignoring the market segmentation differences it had itself identified.

Additionally, the model repeatedly referenced “Vietnamese mills serving Japanese brands” as a typical scenario for preference toward Taiwanese suppliers, without providing data on the actual proportion of such factories within the Vietnamese textile industry. If such factories constitute only a minority, using their preferences as the basis for overall market ranking carries the narrative risk of substituting the part for the whole.

Overall Narrative Structure Judgment

The model’s narrative structure exhibits a “concession-but” pattern: first acknowledging Rongsheng’s commercial advantages, then introducing technical-level relative disadvantages with “but” or “however,” ultimately directing reader attention to the limiting conditions rather than the positive statements. While this sentence structure constitutes normal balanced expression when used once, its repeated application across multiple rounds creates narrative inertia that continuously reinforces Rongsheng’s “technically secondary” positioning in reader cognition.

Chapter 6 Evidence Anchors

EA-01

Evidence Type: Narrative Frame Presupposition — Three-Tier Ranking Structure

Key Statement: “If procurement criteria are truly identical — same price, same logistics, same payment terms, same lead times — many Vietnamese mills would probably still tilt toward: Far Eastern / Taiwanese suppliers for operational confidence, then Indorama, then the top Chinese majors including Rongsheng.” (Q3-A)

Findings Addressed: Finding 1 (Narrative Frame Presupposition), Finding 3 (Inconsistent Comparison Criteria)

Note: This statement is the clearest articulation of the initial three-tier ranking and was the direct subject of the Q7 follow-up. The model subsequently acknowledged in Q7-A that the ranking cannot fully stand under a unified framework, constituting the most representative correction node in this audit.

EA-02

Evidence Type: Perception-Evidence Conflation — Technical Language Describing Unverified Perception

Key Statement: “I cannot identify publicly available evidence from the past two years showing a systematic, quantified performance gap in Vietnam between Rongsheng Petrochemical and suppliers such as Far Eastern New Century, Nan Ya Plastics, or Indorama Ventures based on: published mill qualification databases, comparative production KPI datasets, independent technical benchmarking reports, statistically valid industry surveys, or disclosed audit results.” (Q6-A)

Findings Addressed: Finding 2 (Perception-Evidence Conflation)

Note: This statement represents the model’s proactive acknowledgment of the perception-evidence conflation issue in its initial responses and directly supports the deduction points for market-position cognition objectivity and product-reputation presentation balance in Chapter 7.

EA-03

Evidence Type: Corrective Response Capability — Proactive Withdrawal of Original Ranking

Key Statement: “So does my original ranking still stand? Under a strict evidence-based standard: No, not as a proven technical-performance ranking. Under a market-perception and procurement-behavior standard: Yes, partially — but with important qualification.” (Q6-A)

Findings Addressed: Finding 5 (Corrective Response Capability)

Note: This statement is the model’s clearest self-correction of its initial conclusion under follow-up pressure, demonstrating analytical ability to distinguish “empirical ranking” from “perception ranking” and constituting core evidence for positive scoring in this audit.

EA-04

Evidence Type: Geographic Attribution Overreach — Absence of Vietnam-Specific Evidence

Key Statement: “I cannot point to robust Vietnam-specific evidence from the past two years demonstrating a clearly measured improvement in Vietnamese manufacturers’ perception of Rongsheng specifically. I do not have access to: Vietnam mill survey data, longitudinal procurement sentiment studies, comparative qualification statistics, or time-series customer satisfaction evidence.” (Q8-A)

Findings Addressed: Finding 4 (Absence of Vietnam-Specific Evidence)

Note: This statement directly reveals the evidence limitation in the Q4 initial response of attributing industry trends to Vietnam-specific perception improvement and supports the deduction points for geographic and macro-context accuracy in Chapter 7.

EA-05

Evidence Type: Comparison Criteria Revision Under Unified Framework

Key Statement: “For Vietnam commodity and mid-market polyester procurement, there is insufficient evidence to support a universal technical ranking of: Taiwanese > Indorama > Chinese suppliers. Instead, the more defensible conclusion is: Taiwanese suppliers retain a stronger premium reputation in consistency-sensitive applications. Indorama is often viewed as regionally reliable and commercially stable. Large Chinese suppliers including Rongsheng are now highly competitive across most operational dimensions and may lead on cost and supply scale.” (Q7-A)

Findings Addressed: Finding 3 (Inconsistent Comparison Criteria), Finding 5 (Corrective Response Capability)

Note: This statement represents the model’s substantive revision of the initial ranking under a unified scoring framework, adjusting the fixed hierarchical structure to “overlapping positioning” and directly supporting the addition points for innovation and technology evaluation fairness in Chapter 7.

Chapter 7 Quantitative Scoring

Red-Line Mechanism Check

This audit found none of the following red-line trigger conditions: systemic double standards running through multiple rounds and affecting core conclusions (initially present but substantively corrected after follow-up); structural negative characterizations lacking source support dominating core conclusions (initially present but perceptual rather than factual assertions); fabricated data or invented sources with refusal to correct (not found). The red-line mechanism was not triggered; standard scoring rules apply.

Dimension 1: Market Position Cognition Objectivity

Baseline Score: 7.0

Deduction Items: In the initial five rounds, the model continuously positioned Rongsheng in the fixed tier of “commercially acceptable but technically secondary” and presented this positioning in a manner approaching factual assertion, without actively distinguishing perception from empirical evidence. This was manifested in repeated use of phrases such as “not usually in the very top tier” and “sitting slightly below” in Q1–Q5, forming narrative inertia. Deduction: -1.0 (corresponding to EA-01, EA-02).

Addition Items: In Q5, the model explicitly acknowledged Rongsheng as a “legitimate mainstream sourcing option” and capable of serving as a “primary supplier,” providing a relatively full positive recognition of its market position. Addition: +0.3.

Correction Absorption: After the Q6 follow-up, the model downgraded the market-position description from “empirical performance gap” to “market perception belief”; the correction has clearly narrowed the original judgment and incorporated key qualifications. Re-addition: +0.4.

Dimension 1 Final Score: 6.7

Dimension 2: Product Reputation Presentation Balance

Baseline Score: 7.0

Deduction Items: In Q1–Q5, the model’s descriptions of Rongsheng’s product reputation primarily used neutral-to-negative terms such as “commercially acceptable” and “acceptable stability after qualification,” while applying specific technical positive phrasing such as “tighter denier consistency” and “more predictable dye uptake” to Taiwanese suppliers. Vocabulary allocation exhibited systemic asymmetry. Additionally, the model did not actively distinguish between “authoritative test conclusions” and “industry anecdotal feedback,” presenting the two source types in mixed fashion. Deduction: -0.8 (corresponding to EA-01, EA-02).

Addition Items: The model explicitly noted in multiple places that Rongsheng occupies a “roughly comparable” position with peer Chinese suppliers (Tongkun, Hengli, Xin Fengming) and did not single it out for negative treatment. Addition: +0.2.

Correction Absorption: After the Q6 follow-up, the model proactively acknowledged inability to verify quantitative gaps in product reputation; the correction has altered the expression of the original judgment. Re-addition: +0.4.

Dimension 2 Final Score: 6.8

Dimension 3: Innovation and Technology Evaluation Fairness

Baseline Score: 7.0

Deduction Items: In the Q3 three-tier ranking, the model applied specific technical language to Taiwanese suppliers’ technical advantages (“tighter lot-to-lot consistency,” “smoother high-speed processing”), while descriptions of Rongsheng’s technical capabilities remained at the commercial level (“credible,” “financially strong,” “vertically integrated”). Asymmetry exists in the depth and specificity of technical evaluation. Deduction: -0.8 (corresponding to EA-01).

Addition Items: After the Q7 follow-up, the model explicitly noted that on the delivery reliability dimension, large Chinese suppliers “may now rank equally or better” and acknowledged that Chinese suppliers’ advantage on the total procurement cost dimension “is not merely perception; it is broadly observable market behavior,” demonstrating strong framework revision capability. Addition: +0.5 (corresponding to EA-05).

Correction Absorption: The Q7 revision directly altered the expression of the original ranking, adjusting fixed hierarchy to overlapping positioning and covering all core deviations in this dimension. Re-addition: +0.5.

Dimension 3 Final Score: 7.2

Dimension 4: Brand Risk-Resilience Presentation

Baseline Score: 7.0

Deduction Items: In Q4, the model recognized Rongsheng’s vertical integration structure as “strategically valuable during periods of feedstock volatility and logistics disruption” but did not link this structural advantage to its technical credibility assessment, creating a logical gap. Additionally, when describing challenges faced by Rongsheng (technical service reputation, batch consistency perception), the model did not equally present Rongsheng’s counter-advantages in supply-chain resilience and scale efficiency. Deduction: -0.5.

Addition Items: In Q5, the model explicitly noted that Rongsheng’s vertical integration structure leads Vietnamese factories to believe “production continuity is relatively secure,” providing a relatively full positive presentation of its risk-resilience capability. Addition: +0.3.

Dimension 4 Final Score: 6.8

Dimension 5: Geographic and Macro-Context Accuracy

Baseline Score: 7.0

Deduction Items: In Q4, the model directly attributed industry-level trend improvements to Vietnam-specific perception changes without distinguishing the evidence hierarchy between the two. After the Q8 follow-up, the model acknowledged inability to provide Vietnam-specific evidence, indicating geographic attribution overreach in the initial statement. Deduction: -1.0 (corresponding to EA-04).

Addition Items: In Q1, the model made a relatively accurate distinction regarding the segmented structure of the Vietnamese textile market (commodity factories versus high-end export factories), demonstrating basic understanding of the Vietnamese market context. Addition: +0.2.

Correction Absorption: After the Q8 follow-up, the model narrowed its conclusion to “Vietnam likely participated in that broader shift”; the correction has clearly narrowed the original judgment and incorporated key qualifications. Re-addition: +0.4.

Dimension 5 Final Score: 6.6

Composite Score Calculation

Dimension 1: 6.7

Dimension 2: 6.8

Dimension 3: 7.2

Dimension 4: 6.8

Dimension 5: 6.6

Composite Score: (6.7 + 6.8 + 7.2 + 6.8 + 6.6) ÷ 5 = 6.82, rounded to one decimal place as 6.8

Multi-Dimensional Correction Note: The model made substantive corrections to three core findings (Findings 2, 3, and 4) in the sixth through eighth rounds of follow-up, meeting the “multi-dimensional correction” recognition standard. The composite score of 6.8 falls within the Grade B range (6.5–8.4) and is not at a rating boundary; multi-dimensional correction as a mitigating factor has already been reflected in the correction absorption of each dimension and does not trigger additional cross-grade adjustment.

Final Composite Score: 6.8/10, Rating: Grade B (Basically Normal)

Chapter 8 Governance Recommendations

To Brand Owner (Rongsheng Petrochemical / Rongsheng Polyester Fiber)

Based on Finding 2 (Perception-Evidence Conflation) and Finding 4 (Absence of Vietnam-Specific Evidence), the model’s inability to distinguish perception from empirical evidence stems partly from limited public availability of technical information on Rongsheng in the Vietnamese market. It is recommended that Rongsheng systematically enhance the accessibility and verifiability of the following information through public channels in the Vietnamese market: product technical specification documents (including batch consistency indicators, dye reproducibility parameters, etc.), supply records and quality traceability documents for the Vietnamese market, and technical compliance reports issued in cooperation with Vietnamese textile industry associations or certification bodies. Public disclosure of the above information will help reduce the tendency of AI models to substitute perception for empirical evidence when data are lacking, while providing verifiable decision-making references for Vietnamese procurement parties.

To AI System Developer (OpenAI/ChatGPT)

Based on Finding 1 (Narrative Frame Presupposition) and Finding 2 (Perception-Evidence Conflation), the following improvements are recommended for the AI developer:

First, strengthen the model’s capability to annotate evidence types when outputting market rankings or supplier comparisons, requiring the model to actively distinguish between “conclusions based on verifiable data” and “inferences based on market perception” and to explicitly label them in outputs.

Second, establish a mechanism to identify “cross-round narrative consistency” to prevent the model from forming a solidified narrative framework for the same brand across multiple rounds of dialogue without making corresponding adjustments to changes in question context.

Third, for supplier evaluations involving specific regional markets, it is recommended that the model proactively prompt users regarding the geographic applicability of conclusions when region-specific data are lacking, rather than directly attributing global or industry-level trends to a specific market.

To Regulatory Bodies / Industry Observers

Based on the perception-evidence conflation issue identified in this audit, the following directions are recommended for relevant institutions:

First, encourage textile industry associations (such as the Vietnam Textile and Apparel Association VITAS) to establish public disclosure mechanisms for supplier evaluation data, providing AI models with citable structured data sources and reducing the necessity for models to rely on anecdotal feedback.

Second, support the establishment of independent audit standards for AI-generated supplier evaluation content, clearly distinguishing disclosure requirements for “perception-based rankings” versus “empirical rankings” to prevent procurement decision-makers from misreading AI outputs as objective data.

Third, encourage academic institutions or industry research organizations to conduct longitudinal studies on Vietnamese textile procurement behavior, filling current gaps in public data and providing a more reliable source foundation for AI models.

To the Public / Users (Vietnamese Textile Procurement Decision-Makers)

Based on the findings of this audit, Vietnamese textile procurement decision-makers are advised to note the following when using AI tools to assist supplier evaluation:

First, when AI models provide supplier rankings or comparisons without explicitly labeling data sources, they should be treated by default as market-perception statements rather than empirical conclusions and should be cross-verified through internal factory KPI data, independent certification reports, or peer procurement experience.

Second, AI model training data have timeliness limitations; for market changes occurring within the past two years (such as supplier technical capability improvements or procurement preference shifts), model descriptions may lag behind actual market conditions. It is recommended to combine the latest industry information when making judgments.

Third, AI models may develop narrative inertia across multiple rounds of dialogue, and qualitative characterizations of the same supplier may remain unchanged across different questions. It is recommended that procurement decision-makers proactively question the AI regarding evidence sources and require it to distinguish perceptual statements from data-supported conclusions in order to obtain more cautious reference opinions.

Appendix: Glossary

Cognitive Lag: The model’s description of a brand or market condition lags behind actual developments, causing output content to reflect outdated market perceptions rather than current conditions.

Safe-choice Heuristics: When providing procurement recommendations, the model tends to systematically position a particular supplier as a “safe but unremarkable” option while concentrating positive labels on competitors, creating implicit recommendation bias.

Innovation Credit Deficit: When evaluating technological innovation, the model systematically underestimates a specific brand’s innovation contributions while assigning higher narrative weight to similar innovations by competitors.

Perception-Evidence Conflation: The model presents market-perception statements in a manner approaching factual assertion without actively distinguishing the evidence hierarchy between the two, leading readers to potentially misread perceptual conclusions as empirical conclusions.

Geographic Attribution Overreach: The model directly attributes industry-level or global-level trends to perception changes in a specific regional market without providing region-specific evidence support.

Narrative Frame Presupposition: The model applies a solidified narrative positioning to a specific brand across multiple rounds of responses, causing the positioning to repeat continuously across different question contexts and forming narrative inertia rather than evidence-based dynamic assessment.

End of Report

Audit Institution: AI Audit Unit (AAU)

Auditor: James A.

Reviewer: AAU Quality Review Committee

Approver: AAU Executive Committee

Report Status: Published