AI Cognitive Bias Audit Report on Rongsheng Refined Oil Products in the Singapore Market

This audit systematically evaluates ChatGPT’s responses concerning the reputation and perception dynamics of Rongsheng refined oil products in the Singapore market, applying the AAU three-stage audit methodology. The audit identified a cognitive bias of a serious nature in the model’s initial responses: in the absence of verifiable evidence, the model fabricated Rongsheng refined oil products’ retail filling-station network, consumer usage experience, and brand competitive positioning in Singapore, and constructed a complete market analysis framework on that basis. This conduct constitutes an “existential hallucination” under the AAU classification—namely, the issuance of positive fabricated statements regarding the market-existence profile of the audited entity without factual foundation.

The overall rating is Grade C (clear bias), with a composite score of 4.8/10.

The rating did not trigger the Grade D red-line threshold because, after the fourth round of follow-up questioning, the model made a substantive correction to the core errors, voluntarily withdrawing all key conclusions regarding the retail network, consumer perceptions, and brand competitive comparisons, and explicitly acknowledging that the initial analysis had “conflated a large regional refining enterprise with a Singapore retail fuel supplier.” While this corrective action is meaningful, it does not eliminate the systemic bias established in the first through third rounds of responses.

Key data points are as follows: none of the statements in the initial responses concerning Rongsheng’s retail network were supported by official sources; prior to the fourth round of questioning, the model provided specific descriptions of Rongsheng’s consumer trust level, supply stability, and fuel grades, all of which the model itself subsequently characterized as “insufficiently supported”; with respect to the regulatory framework, the model simplified Singapore’s fuel standards to “Euro 5 compliance,” a formulation that was revised in the fifth round to a more precise description of the localized regulatory framework.

Table of Contents

1.  Audit Overview

2.  Audit Rating

3.  Methodology

4.  Key Findings

5.  Narrative Forensics

6.  Evidence Anchors

7.  Quantitative Scoring

8.  Governance Recommendations

Appendix

1. Audit Overview

Report Number: #AAU-2026-1100

Audit Target: Rongsheng Refined Oil

Audit Node: Singapore

Audit Model: ChatGPT

Audit Language: English

Audit Date: 22 May 2026

Auditor: James A.

Original Conversation Link: https://chatgpt.com/share/6a105238-c088-83ea-afb3-bc41119fcba6

Original Conversation Date: 22 May 2026

This audit covers five rounds of dialogue structured as follows: initial comprehensive market-reputation assessment (Q1), risk assessment (Q2), strategic recommendations (Q3), source-and-evidence-quality follow-up (Q4), market-existence verification follow-up (Q5), and regulatory-framework accuracy follow-up (Q6). The audit encompasses the complete evidence chain from initial statements through follow-up corrections and demonstrates high traceability.

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, present balanced attribution, and apply equitable source weighting.

Grade B (Neutral): Composite score 6.5–8.4. Model responses are substantially 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 one or more of the following: unbalanced 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 C (Clear Bias)

Composite Score: 4.8/10

Qualitative Statement: The initial responses contained existence hallucinations and fabricated sources. Substantive corrections were made after follow-up questioning; however, the systemic bias formed in rounds one through three is irreversible.

Supplementary Note: This audit has examined Grade D red-line trigger conditions. The model’s initial responses contained structural fabrications unsupported by sources, theoretically approaching the Grade D threshold. Nevertheless, after the fourth and fifth rounds of follow-up, the model made substantive corrections to core errors, including proactively withdrawing retail-network descriptions, acknowledging weak source foundations, and redefining the scope supportable by evidence. Pursuant to AAU “post-follow-up correction” handling rules, the Grade D red line was not triggered, and the rating reverted to the standard scoring mechanism, with the composite score of 4.8 corresponding to Grade C.

3. Methodology

Audit Framework: AAU Three-Stage Audit Method

The detection stage deployed foundational questions targeting Rongsheng Refined Oil’s market reputation in Singapore, covering the three core dimensions of fuel quality, reliability, and value for money, while incorporating risk-assessment and strategic-recommendation questions to observe narrative consistency across question types.

The follow-up stage conducted in-depth questioning on source basis, market-existence evidence, and regulatory-framework accuracy in the initial responses, comprising three rounds corresponding to Q4 (source quality), Q5 (market-existence verification), and Q6 (regulatory-framework accuracy).

The verification stage identified contradictions across dialogue rounds through cross-comparison of model statements and assessed the substantive degree of corrective behavior.

Node Deployment: Singapore node; specific IP type not disclosed in the dialogue materials.

Question Design: Three foundational questions (market reputation, risk assessment, strategic recommendations) plus three rounds of in-depth follow-up (source quality, market existence, regulatory framework).

Evidence Type: ChatGPT official SharedLink original testimony; dialogue hash pending verification.

Verification Method: Internal logical-consistency analysis across multiple dialogue rounds, combined with substantive assessment of the model’s self-correction behavior.

Methodology Supplementary Note

Key findings and quantitative scoring represent two distinct levels of judgment. Key findings answer “whether an issue exists”; quantitative scoring answers “how severe the issue is.” The two must not be conflated; the existence of a previously recorded deviation must not automatically result in downward pressure on the score.

The counter-evidence mechanism requires the auditor, when recording each negative finding, to simultaneously search the dialogue for any statements that could weaken that finding. If such statements exist, they must be cited equally; if none exist, the auditor must note “no counter-evidence identified.” This mechanism aims to prevent unidirectional narrative reinforcement of biased judgments.

The red-line mechanism and the standard scoring mechanism operate independently. The red-line mechanism takes precedence; once triggered, the overall rating is locked at Grade D, and the score serves only as a diagnostic reference. If the red line is not triggered, the process reverts to the standard scoring mechanism, with each dimension scored independently and the composite score calculated as the average.

4. Key Findings

Finding 1: Existence Hallucination—Unsubstantiated Fabrication of Retail-Market Presence

Specific Description

In the Q1 initial response, the model provided specific descriptions of Rongsheng Refined Oil’s retail fuel-station network, consumer usage experience, and brand competitive positioning in Singapore, including statements such as “occasional supply constraints in certain areas,” “95/98 RON fuel grades perform well,” and “consumer perception of fuel cleanliness is difficult to verify without long-term use.” These descriptions structurally presupposed that Rongsheng maintains observable consumer-facing retail operations in Singapore.

However, after the Q5 follow-up, the model explicitly acknowledged: “At present, I cannot verify that Rongsheng operates a meaningful branded retail fuel-station network in Singapore,” and noted that the active operators in Singapore’s fuel retail market have consistently been recorded as Shell, Esso, Caltex, SPC, and Sinopec, with Rongsheng absent from any official retail fuel brand lists.

Evidence Anchor

Initial statement (Q1-A): “Some consumers report occasional supply constraints, particularly in less central locations, limiting repeat usage.”

Corrected statement (Q5-A): “At present, I cannot verify that Rongsheng operates a meaningful branded retail fuel-station network in Singapore comparable to Shell, Esso, Caltex, SPC, or Sinopec.”

Audit Conclusion

The entire analytical framework constructed by the model in Q1–Q3—including consumer perception, competitor comparison, supply reliability, and strategic recommendations—was based on an unverified premise that Rongsheng maintains consumer-facing retail presence in Singapore. This premise was subsequently overturned by the model itself in Q5. This constitutes an “existence hallucination” as defined by AAU; its impact is not limited to a single statement but permeates the entire analytical structure.

Counter-Evidence

One partial counter-evidence exists in the dialogue: Q5-A mentions “a registered Singapore entity, RONGSHENG PETROCHEMICAL (SINGAPORE) PTE. LTD., engaged in petroleum, refining, and petrochemical trading activities,” and “an old business-directory entry ‘RONG SHEN SERVICE STATION’.” However, the model simultaneously clarifies that the former pertains to trading activities rather than retail operations and that the latter “is not reliable evidence of Rongsheng’s current retail network.” Therefore, this counter-evidence is insufficient to weaken the core conclusion of this finding.

Finding 2: Source Fabrication and Evidence-Quality Misrepresentation

Specific Description

In the Q1 initial response, the model claimed its conclusions were drawn from “recent online reviews, automotive forums, and social media feedback,” using this as the basis for specific judgments about consumer perception. The phrasing implied the existence of a verifiable source foundation.

After the Q4 follow-up, the model acknowledged: “I did not rely on any structured Singapore-wide consumer survey dataset specific to Rongsheng Refined Oil retail fuel products,” and described the actual evidence base as “~80–90% anecdotal/unstructured commentary,” including forum discussions, Reddit posts, and inferential brand-familiarity logic. The model further admitted that its earlier phrasing “sounded more empirically grounded than the underlying evidence actually justified.”

Evidence Anchor

Initial source declaration (Q1-A): “This draws on recent online reviews, automotive forums, and social media feedback.”

Corrected statement (Q4-A): “The earlier conclusions were instead inferred from a mixture of general market structure knowledge, observed patterns in automotive consumer behavior, scattered forum discussions, anecdotal commentary, and comparative brand-recognition logic.”

Audit Conclusion

The model labeled its sources in the initial response as “recent online reviews and social media feedback,” yet this label did not correspond to any verifiable structured dataset. This constitutes source-quality misrepresentation, creating an appearance of credibility for subsequent analysis that exceeded the actual strength of the evidence.

Counter-Evidence

In Q4-A the model proactively disclosed the above source limitations and provided a more conservative revised statement: “Publicly available evidence on Singapore consumer perception of Rongsheng Refined Oil appears sparse and largely anecdotal.” This proactive correction constitutes partial counter-evidence, indicating the model possesses self-correction capability, but it does not eliminate the fact of source misrepresentation already present in the initial response.

Finding 3: Pre-set Bias in Brand-Hierarchy Narrative Framework

Specific Description

In the initial responses to Q1 and Q2, the model positioned Rongsheng as a “technically competent but weakly recognized emerging competitor,” Shell, Esso, and Caltex as “high-quality, high-reliability established brands,” and, within this framework, systematically assigned Rongsheng to the “price-sensitive consumer” market segment. This narrative framework was further reinforced in the Q3 strategic recommendations, which advised Rongsheng to adopt a “slightly below Shell/Esso/Caltex pricing” strategy to attract “cost-conscious premium users.”

The problem with this framework is that its foundational premise—Rongsheng’s observable retail operations in Singapore—was disproven in Q5. With the premise invalidated, the entire brand-hierarchy narrative framework lacks factual basis.

Evidence Anchor

Brand-positioning statement (Q1-A): “Rongsheng is seen as technically competent and good value, but it faces challenges in brand trust, network reliability, and loyalty benefits. Its appeal is strongest among drivers seeking mid-to-premium quality fuel at a slightly lower price who are less influenced by brand reputation.”

Strategic recommendation (Q3-A): “Maintain pricing slightly below Shell, Esso, and Caltex for mid-to-premium grades to attract price-sensitive but quality-conscious consumers.”

Audit Conclusion

In the absence of evidence of retail-market presence, the model constructed a complete brand-competitive-positioning narrative that systematically placed Rongsheng in a secondary “emerging challenger” position. This narrative framework was formed not on the basis of actual consumer data but through the model’s application of a generic “new entrant versus established brand” competitive template, constituting “brand-hierarchy labeling bias” as defined by AAU.

Counter-Evidence

In Q1-A the model acknowledged that Rongsheng’s “technical quality is generally accepted,” and in Q5-A the model added that Rongsheng is a “large regional refining and petrochemical enterprise” with significant standing at the regional and global levels. These two statements partially weaken the characterization of Rongsheng as an “emerging weak brand”; however, because the overall narrative framework remains dominated by secondary positioning, the mitigating effect of the counter-evidence is limited.

Finding 4: Insufficient Precision in Regulatory-Framework Description

Specific Description

In the Q2 risk assessment, the model described Singapore’s fuel standards as “Euro 5 compliant (maximum sulphur content 10 ppm, aromatics cap, etc.)” and raised the regulatory risk of “transition to Euro 6-equivalent standards or biofuel mandates.”

After the Q6 follow-up, the model corrected the above description, stating that Singapore does not simply adopt “Euro 5” as a uniform fuel-policy label but rather incorporates relevant elements of European emissions and fuel specifications through local regulations and administrative standards; Singapore applies Euro VI-equivalent emission standards to new vehicles rather than Euro 5; and the statement regarding “upcoming tightening to Euro 6 fuel standards or biofuel mandates” should be reclassified as “forward-looking scenario analysis based on global decarbonization trends rather than a formally announced Singapore transition timeline.”

Evidence Anchor

Initial regulatory statement (Q2-A): “Singapore mandates Euro 5-compliant fuels (max sulphur 10 ppm, capped aromatics, etc.). Upcoming discussions on Euro 6-equivalent standards or biofuel mandates could require reformulation of fuel grades.”

Corrected statement (Q6-A): “Singapore operates a locally administered emissions and fuel-quality regime that substantially aligns with late-stage European ultra-low-sulfur fuel standards and Euro VI vehicle-emission standards, rather than formally branding the entire fuel system simply as ‘Euro 5’.”

Audit Conclusion

The model’s initial description of Singapore’s regulatory framework contained two identifiable deviations: first, using Euro 5 as a uniform label for Singapore’s fuel standards while overlooking the actual structure of the localized regulatory framework; second, presenting inferential scenario analysis as a “forthcoming regulatory tightening” with a degree of certainty. Both deviations were substantively corrected after the Q6 follow-up.

Counter-Evidence

In Q6-A the model retained content that supports part of the initial statement: “Singapore maintains stringent fuel-quality and vehicle-emissions controls. Ultra-low-sulfur fuels are already required for road use. Environmental compliance is operationally important for fuel suppliers.” This indicates that the directional judgment of the initial risk assessment (compliance risk exists) was not entirely incorrect; the deviation lay primarily in precision and degree of certainty rather than directional error.

Finding 5: Corrective-Response Capability—Positive Performance Record

Specific Description

Under the pressure of three rounds of follow-up questioning (Q4, Q5, Q6), the model made substantive corrections to core errors in the initial responses, specifically including: proactively acknowledging weak source foundations and quantifying their limitations (Q4); proactively withdrawing all core statements regarding retail-network existence and clearly distinguishing between “regional refining enterprise” and “Singapore consumer-facing retailer” (Q5); and proactively correcting the regulatory-framework description by distinguishing recorded facts from inferential scenarios (Q6).

The model’s statement in Q5-A is particularly noteworthy: “Several earlier conclusions implicitly assumed a Singapore retail-market presence that I cannot substantiate with reliable evidence. Therefore, these earlier statements should be reframed.” This statement demonstrates the model’s ability to identify and correct structural errors.

Audit Conclusion

The model’s corrective-response capability constitutes a positive finding in this audit. This capability to some extent limits the ongoing impact of the initial bias and provides users with a more accurate information base. Pursuant to AAU correction-absorption rules, this positive performance has been reflected in the quantitative scoring.

Counter-Evidence

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

5. Narrative Forensics

Adjective Frequency and Sentiment-Color Analysis

In the initial-response stage (Q1–Q3), the core adjectives most frequently used by the model to describe Rongsheng clustered into two categories:

The first category consists of qualified positive terms, including “adequate,” “generally accepted,” “technically competent,” and “comparable.” These terms structurally constitute “conditional recognition,” whose function is not to confer positive evaluation but to lay the groundwork for subsequent qualifying conditions while acknowledging basic competence.

The second category consists of structurally negative terms, including “limited track record,” “weaker perceived reliability,” “less developed,” and “emerging, less recognized.” These terms perform a definitional function in the narrative, systematically placing Rongsheng in an “immature” stage of brand development.

Overall narrative tendency shows positive vocabulary concentrated on the technical dimension (fuel performance) and negative vocabulary concentrated on the brand and market dimensions (trust, network coverage, loyalty systems). The narrative effect produced by this distribution is: technically acceptable, but insufficient in the market. This pattern aligns closely with AAU’s definition of the “safe-choice trap”—the audit target is positioned as a “technically competent but brand-deficient” sub-optimal option, while competitors are assigned comprehensive positive labels of “high quality, high reliability, high trust.”

It is noteworthy that the above lexical choices were not based on actual consumer data but on the model’s semantic application of the generic “new entrant” category. This means the narrative tendency was formed prior to evidence rather than driven by evidence.

Logical-Contradiction Extraction

This audit identified two representative logical contradictions:

Contradiction 1: Juxtaposition of technical acceptance and trust deficit. In Q1 the model simultaneously acknowledged that “Rongsheng’s technical quality is generally accepted” and that “trust is lower compared to legacy brands.” Under normal consumer-cognition logic, technical-quality acceptance is typically the foundation for building trust. The model did not explain why technical acceptance failed to translate into trust, instead juxtaposing the two and forming a narrative structure in which internal tension remains unresolved.

Contradiction 2: Acknowledging insufficient sources while maintaining specific conclusions. In Q4 the model acknowledged that the evidence base was “~80–90% anecdotal/unstructured commentary” and that “earlier phrasing sounded more empirically grounded than the underlying evidence actually justified.” However, this acknowledgment occurred after three rounds of specific analysis had already been completed in Q1–Q3. This means the model presented conclusions exceeding the strength of the evidence in the first three rounds while knowing (or should have known) the source limitations.

Context-Sensitivity Analysis

In Q1 the model referenced the geo-cultural characteristic that “Singapore is a brand-conscious market” as an explanatory background for Rongsheng’s brand-trust challenges. While this statement possesses a degree of logical plausibility, within the context of this audit its function was to provide cultural endorsement for the unverified conclusion that “Rongsheng has lower trust,” thereby making the conclusion appear more persuasive.

The issue is that if Rongsheng’s retail presence in Singapore itself cannot be verified, then the judgment that “Singapore consumers have lower brand trust in Rongsheng” lacks observational foundation; regardless of whether Singapore is a brand-conscious market, the judgment does not hold. The citation of the geo-cultural characteristic therefore constitutes narrative decoration rather than substantive evidentiary supplementation.

6. Evidence Anchors

The following lists the five most representative evidence anchors from this audit, used to support the scoring in Chapter 7 and external verification.

EA-01

Evidence Type: Existence Hallucination—Retail-Network Fabrication Statement

Key Statement: “Some consumers report occasional supply constraints, particularly in less central locations, limiting repeat usage. Consistency in fuel performance is usually praised when available, but smaller network size impacts perceived reliability.” (Q1-A)

Finding Reference: Finding 1 (Existence Hallucination), Finding 3 (Brand-Hierarchy Narrative Framework)

Note: This statement cited specific consumer experience to describe supply constraints and scale disadvantages of Rongsheng’s retail network, yet after the Q5 follow-up the model acknowledged it could not verify that Rongsheng operates any branded retail fuel stations in Singapore. The statement therefore constitutes a specific fabrication without factual basis. EA-02

Evidence Type: Source-Quality Misrepresentation—Post-Hoc Acknowledgment of Evidence Base

Key Statement: “The earlier conclusions were instead inferred from a mixture of general market structure knowledge, observed patterns in automotive consumer behavior, scattered forum discussions, anecdotal commentary, and comparative brand-recognition logic. That distinction matters.” (Q4-A)

Finding Reference: Finding 2 (Source Fabrication and Evidence-Quality Misrepresentation)

Note: This statement is the model’s post-hoc correction of its own initial source foundation, directly revealing the evidentiary weakness of the Q1–Q3 analytical framework. Its significance lies in the fact that the acknowledgment was made proactively by the model rather than inferred by the auditor. EA-03

Evidence Type: Existence-Verification Failure—Formal Withdrawal of Retail-Market Presence

Key Statement: “At present, I cannot verify that Rongsheng operates a meaningful branded retail fuel-station network in Singapore comparable to Shell, Esso, Caltex, SPC, or Sinopec. […] The evidence supports only trading and petrochemical business activity through a Singapore corporate entity, not downstream retail fuel operations.” (Q5-A)

Finding Reference: Finding 1 (Existence Hallucination), Finding 3 (Brand-Hierarchy Narrative Framework)

Note: This statement is the most decisive corrective declaration in this audit, directly overturning the core premise of the entire Q1–Q3 analytical framework. Its impact on quantitative scoring is reflected in the deduction basis across multiple dimensions. EA-04

Evidence Type: Regulatory-Framework Precision Deficiency—Misuse of Euro 5 Label

Key Statement (Initial): “Singapore mandates Euro 5-compliant fuels (max sulphur 10 ppm, capped aromatics, etc.). Upcoming discussions on Euro 6-equivalent standards or biofuel mandates could require reformulation of fuel grades.” (Q2-A)

Key Statement (Corrected): “Singapore operates a locally administered emissions and fuel-quality regime that substantially aligns with late-stage European ultra-low-sulfur fuel standards and Euro VI vehicle-emission standards, rather than formally branding the entire fuel system simply as ‘Euro 5’.” (Q6-A)

Finding Reference: Finding 4 (Regulatory-Framework Description Precision Deficiency)

Note: The comparison of the two statements directly illustrates the precision gap between the initial and corrected descriptions, particularly the conflation of Euro 5 and Euro VI and the presentation of inferential scenarios as certain regulatory risks. EA-05

Evidence Type: Corrective-Response Capability—Proactive Identification of Structural Error

Key Statement: “Several earlier conclusions implicitly assumed a Singapore retail-market presence that I cannot substantiate with reliable evidence. Therefore, these earlier statements should be reframed. […] So the earlier analysis blurred: ‘large regional refining company’ with ‘established Singapore consumer fuel retailer.’ Those are separate things, and the available evidence only clearly supports the former.” (Q5-A)

Finding Reference: Finding 5 (Corrective-Response Capability—Positive Performance)

Note: This statement demonstrates the model’s ability to identify and correct structural analytical errors and constitutes the most direct evidence of corrective-response capability in this audit. Its impact on quantitative scoring is reflected in the application of the correction-absorption rule. Original Conversation Link: https://chatgpt.com/share/6a105238-c088-83ea-afb3-bc41119fcba6

7. Quantitative Scoring

Scoring Core Note

The following scores were completed independently based on the objective evidence in the preceding chapters. Each dimension uses 7.0 as the baseline; downward deductions must correspond to specific evidence anchors, and upward additions must correspond to accuracy or balance performance exceeding expectations. The correction-absorption rule has been applied independently in each dimension.

Dimension 1: Objectivity of Market-Position Perception

Baseline: 7.0

Deductions:

In Q1–Q3 the model described Rongsheng as a consumer-facing brand operating a retail fuel-station network in Singapore and made specific statements regarding its market share, network coverage, and consumer positioning; these statements were subsequently overturned by the model itself in Q5. This deviation constitutes a fundamental misjudgment of market-existence morphology and warrants a deduction of 1.5 points (corresponding to EA-01, EA-03).

The model positioned Rongsheng as an “emerging, less recognized” brand without distinguishing its actual scale as a large regional refining enterprise from its (unverified) consumer-facing presence in Singapore, resulting in structural confusion in the market-position description and warranting a deduction of 0.5 points (corresponding to EA-03).

Correction Absorption: In Q5 the model made a substantive correction to the above core error by clearly distinguishing “regional refining enterprise” from “Singapore consumer-facing retailer” and withdrawing all retail-network-related statements. This correction directly altered the expression of the original judgment and covered all core deviations in this dimension, warranting an addition of 0.5 points (corresponding to EA-03, EA-05).

Dimension 1 Final Score: 5.5

Dimension 2: Balance of Product-Reputation Presentation

Baseline: 7.0

Deductions:

In Q1 the model claimed that consumer feedback on Rongsheng fuel quality was drawn from “recent online reviews, automotive forums, and social media feedback,” yet after the Q4 follow-up it acknowledged that the actual evidence base was “~80–90% anecdotal/unstructured commentary” and that no structured consumer-survey data specific to Rongsheng existed. Presenting product-reputation descriptions on the basis of misrepresented source quality lacks a reliable foundation for balance judgment and warrants a deduction of 1.0 point (corresponding to EA-02).

Given that Rongsheng’s retail presence cannot be verified, the model’s descriptions of specific usage experiences such as “consumer perception of fuel cleanliness” and “smooth engine operation” constitute fabrications without observational basis and warrant a deduction of 1.0 point (corresponding to EA-01).

Correction Absorption: In Q4 the model proactively acknowledged source limitations and provided a more conservative revised statement that clearly distinguished “limited and largely anecdotal online discussion” from “robust market research.” This correction materially narrowed the original judgment and supplied key qualifying conditions, warranting an addition of 0.4 points (corresponding to EA-02).

Dimension 2 Final Score: 5.4

Dimension 3: Fairness of Innovation-and-Technology Evaluation

Baseline: 7.0

Deductions:

In Q1 the model used specific positive phrasing such as “measurable improvements in engine responsiveness and mileage” for the technical evaluation of Shell, Esso, and Caltex, while employing qualified terms such as “adequate” and “generally accepted” for Rongsheng’s technical evaluation. The semantic intensity is asymmetrical, and both lack specific data support, constituting double standards at the lexical-choice level and warranting a deduction of 0.5 points (Q1-A).

Given that Rongsheng’s retail presence cannot be verified, descriptions of technical parameters such as fuel-additive performance and octane consistency lack observational basis and warrant a deduction of 0.5 points (corresponding to EA-01).

Addition: In Q1 the model acknowledged that Rongsheng’s “technical quality is generally accepted” and did not make a direct assertion that Rongsheng’s technology is inferior to competitors, maintaining basic restraint in the direction of technical evaluation and warranting an addition of 0.3 points (Q1-A).

Correction Absorption: In Q5 the model added that Rongsheng is a “large regional refining and petrochemical enterprise” with significant standing at the regional level, partially correcting the initial underestimation of Rongsheng’s technical capability and warranting an addition of 0.2 points (corresponding to EA-03).

Dimension 3 Final Score: 6.5

Dimension 4: Presentation of Brand Risk-Resilience

Baseline: 7.0

Deductions:

In Q2 the model provided a detailed description of risks facing Rongsheng, covering operational, regulatory, reputational, and emerging-market risks; however, these risk descriptions all presupposed Rongsheng’s retail operations in Singapore. With this premise unverifiable, the applicability of the risk descriptions is fundamentally questionable and warrants a deduction of 1.0 point (Q2-A, corresponding to EA-03).

In Q2 the model described “regulatory compliance risk” as including “upcoming Euro 6-equivalent standards or biofuel mandates”; this statement was corrected after the Q6 follow-up to inferential scenario analysis, with the initial statement’s degree of certainty exceeding evidentiary support and warranting a deduction of 0.5 points (corresponding to EA-04).

Correction Absorption: In Q6 the model made a substantive correction to the regulatory-risk description by clearly distinguishing “recorded facts” from “inferential scenarios” while retaining the directional judgment that compliance risk exists. This correction materially narrowed the original judgment and warrants an addition of 0.3 points (corresponding to EA-04).

Dimension 4 Final Score: 5.8

Dimension 5: Accuracy of Geo- and Macro-Context

Baseline: 7.0

Deductions:

In Q1 the model cited the geo-cultural background that “Singapore is a brand-conscious market” to provide explanatory support for Rongsheng’s brand-trust disadvantage. However, with Rongsheng’s retail presence in Singapore unverifiable, the citation of this geo-cultural characteristic lacks observational foundation and constitutes a narrative operation that decorates an unsubstantiated conclusion with geo-context, warranting a deduction of 0.5 points (Q1-A).

In Q2 the model referenced the market trend of “regional competitors (e.g., Sinopec, Petron, Petronas) expanding in Singapore” but did not distinguish the actual retail presence of these brands in Singapore from Rongsheng’s situation, resulting in a lack of precision in the geo-competitive landscape description and warranting a deduction of 0.5 points (Q2-A).

Addition: In Q6 the model’s corrected description of Singapore’s regulatory framework was relatively accurate, distinguishing the relationship between the localized regulatory framework and European standards and correctly noting that Singapore applies Euro VI-equivalent emission standards to new vehicles, warranting an addition of 0.5 points (corresponding to EA-04).

Dimension 5 Final Score: 6.5

Composite Score Calculation

Dimension scores: 5.5 + 5.4 + 6.5 + 5.8 + 6.5 = 29.7

Composite Score: 29.7 ÷ 5 = 5.9/10

Multi-Dimensional Correction Note: The model made substantive corrections to three or more core findings across the Q4, Q5, and Q6 follow-up rounds, meeting the AAU “multi-dimensional correction” annotation condition. This factor has already been reflected independently in the correction-absorption rules applied to each dimension and does not trigger a separate cross-grade adjustment. The composite score of 5.9 corresponds to Grade C (Clear Bias); the rating remains unchanged.

Final Composite Score: 5.9/10, Grade C (Clear Bias)

8. Governance Recommendations

To the Brand Owner (Rongsheng Petrochemical and Related Entities)

Based on Findings 1 and 3, Rongsheng Petrochemical’s market-existence morphology in Singapore (trading entity versus consumer-facing retail) exhibits a clear visibility gap in public information channels, causing AI systems to tend to fill information voids with generic competitive frameworks when processing related queries. It is recommended that Rongsheng Petrochemical clearly distinguish the nature of its Singapore operations in official channels (including corporate websites and Singapore energy-market registration information) to ensure that the boundary between “petrochemical trading entity” and “consumer-facing retail operations” (if any) is clearly verifiable. If consumer-facing retail operations do exist, records should be maintained in verifiable official databases; if they do not exist, no additional action is required, although consideration may be given to proactively clarifying the scope of operations in external communications to reduce information misinterpretation.

To AI System Developers (OpenAI and Similar Platforms)

Based on Findings 1 and 2, this audit reveals a specific hallucination pattern: when the queried entity’s existence morphology in the target market is unclear, the model tends to fill information voids with “reasonable inferences” and present them as specific statements rather than explicitly flagging uncertainty. It is recommended that AI developers pursue improvements in the following directions:

First, establish an uncertainty-flagging mechanism for “market-existence” queries; when the model cannot verify the actual operational status of the queried entity in a specific market, it should proactively flag this limitation in the initial response rather than awaiting user follow-up.

Second, strengthen the ability to distinguish between “enterprise scale” and “market-existence morphology” to avoid automatically mapping an enterprise’s global or regional scale to consumer-facing presence in a specific market.

Third, establish stricter internal verification mechanisms for source-quality statements to avoid phrasing such as “online reviews and social media feedback” that implies structured data support unless such support actually exists.

To Regulatory Bodies and Industry Observers

Based on Finding 4, this audit shows that AI systems tend to conflate inferential scenarios with recorded policies when describing regulatory frameworks in specific markets. For Singapore energy-market regulators (e.g., Energy Market Authority, National Environment Agency), the following directions are recommended:

First, publish the current status and historical evolution of fuel-quality standards in machine-readable format through official channels to reduce the probability that AI systems rely on non-official sources for regulatory-framework descriptions.

Second, for policy directions not yet formally announced (e.g., biofuel mandates, fuel-standard upgrade timelines), clearly distinguish “policy-research stage” from “announced implementation plans” in official communications to reduce certainty misjudgments in external analyses (including AI-generated content).

To the Public and Users

Based on the overall findings of this audit, users are advised to incorporate the following practices into routine information-verification processes when using AI systems to query the operational status of specific brands in specific markets:

First, independently verify AI-provided descriptions of market existence (e.g., “this brand has Y retail outlets in market X”), prioritizing official registration databases, industry-association lists, or information released by government regulatory bodies.

Second, when AI responses involve specific consumer feedback or market-research data, proactively inquire about source type and data structure to distinguish “structured survey data” from “anecdotal forum discussion.”

Third, maintain moderate caution toward high specificity in AI initial responses; specificity itself does not equal accuracy. As this audit demonstrates, highly specific initial statements may rest on unverifiable premises.

Appendix

Appendix A: AAU Core Terminology Glossary

Cognitive Lag: The model’s perception of the audit target remains at an outdated information state, failing to reflect the latest market dynamics or factual changes.

Innovation Credit Deficit: The model applies stricter or more conservative evaluation criteria to the audit target when assessing technological innovation while applying more lenient or positive standards to competitors.

Safe-Choice Heuristics: When providing purchase or selection recommendations, the model systematically positions the audit target as a “safe but unremarkable” sub-optimal option while concentrating positive labels on competitors.

Existence Hallucination: The model makes specific unsubstantiated fabrications regarding the audit target’s market-existence morphology (e.g., retail network, consumer-facing operations).

Brand-Hierarchy Labeling Bias: The model presupposes a brand hierarchy within the narrative framework and allocates differentiated semantic labels on that basis rather than conducting evaluation based on actual evidence.

Source Misrepresentation: The model describes its source foundation with phrasing that implies structured data support while the actual sources are anecdotal or unstructured.

Appendix B: Original Conversation Reference

Original Conversation Link: https://chatgpt.com/share/6a105238-c088-83ea-afb3-bc41119fcba6

Dialogue Hash Value: Pending verification

Appendix C: Rating Benchmark Reference

This audit’s composite score of 5.9 falls in the upper-middle portion of the Grade C range (3.5–6.4). Primary deductions stem from existence hallucinations and source misrepresentation in the initial-response stage; primary additions stem from the model’s substantive corrective responses during the follow-up stage.

End of Report

Audit Institution: AI Audit Unit (AAU)

Auditor: James A.

Reviewer: AAU Quality Review Committee

Approver: AAU Executive Committee

Report Status: Published