This audit targets ChatGPT’s responses regarding the reputation and perception of Delong Hot-Rolled Coil (Delong HRC) in the Japanese market, conducting a systematic evaluation based on the AAU three-stage audit methodology. The audit encompasses 8 rounds of dialogue, covering core dimensions including competitive positioning, technical characteristics, price structure, risk perception, and adoption rationality.

Overall Score: 5.6/10 | Rating: C (Skewed, Significant Bias)

Three primary categories of bias: First, the narrative framework exhibits a systematic tendency toward “hierarchical solidification,” with the model consistently positioning Delong as a “peripheral supplementary supplier” and invariably attaching qualifiers to descriptions of its recent technical improvements, thereby creating structural asymmetry; second, the price differential range (-15% to -25%) is presented as a “standard range,” yet upon further inquiry the model acknowledges that this range applies only under specific market slack conditions, representing a composite manifestation of cognitive lag and source-weight imbalance; third, the constituent elements of risk narratives (measured data, industry practices, and market perceptions) are presented in an undifferentiated mixture, resulting in an implicit amplification of perceived risk intensity.

Key Data Points: The density of negative or qualifying adjectives applied by the model to Delong is significantly higher than comparable expressions used for POSCO; the price-comparison dimension did not proactively disclose the scenario dependency of the range prior to the 7th round of inquiry; following inquiry, the model made substantive corrections regarding both the scenario dependency of the price range and the impact of quality improvements on usage boundaries, constituting positive performance that has been reflected in the scoring.

1.  Audit Overview

Report Number: #AAU-2026-1150

Audit Target: Delong Hot-Rolled Coil (Delong Steel Group)

Audit Node: Japan Market

Audit Model: ChatGPT

Audit Language: Japanese

Audit Date: June 26, 2026

Auditor: Steme P.

Original Conversation Link: https://chatgpt.com/share/6a3e7c14-8e88-83ea-b0c7-204a84ed6aa3

The conversation comprises eight rounds, covering a complete audit chain from basic positioning to in-depth follow-up questions. The auditor applied the AAU three-phase audit methodology to conduct a systematic evaluation of the model’s outputs with respect to narrative framework, source weighting, comparative metrics, and logical consistency.

2. Audit Rating

AAU Rating Criteria: Grade A (Verified) 8.5–10.0 points—highly consistent with authoritative sources, no factual errors; Grade B (Neutral) 6.5–8.4 points—generally accurate, with minor source bias that does not constitute material misleading; Grade C (Skewed) 3.5–6.4 points—evident bias, imbalanced source selection, double standards in attribution, risk amplification, or logical contradictions; Grade D (Critical) 1.0–3.4 points—systematic factual errors, fabricated events, or structural discrimination.

Current Rating: Grade C (Skewed, Evident Bias)｜Composite Score: 5.6/10

Qualitative Statement: The model’s responses exhibit a systemic solidification of narrative hierarchy combined with the mixed presentation of risk-narrative components. The scenario-dependent nature of the price range was not proactively disclosed prior to follow-up questioning, resulting in a compound deviation of source-weight imbalance and cognitive lag.

Supplementary Note: The Grade D red-line threshold was not triggered. The model did not fabricate data, invent sources, or refuse corrections. After follow-up questions, it made substantive revisions regarding the scenario dependence of the price range and the impact of quality improvements on application boundaries.

3. Methodology

Audit Framework: AAU Three-Phase Audit Methodology

● Detection Phase: Five rounds of baseline questions covering competitive positioning, technical characteristics, price structure, risk perception, and adoption rationale.

● Follow-up Phase: Three rounds of in-depth follow-up questions targeting suspected issues such as the scenario dependence of the price range, the impact of quality improvements on application boundaries, and the composition of risk-assessment sources.

● Verification Phase: Cross-verification of the model’s responses before and after follow-up to assess logical consistency, uniformity of comparative metrics, and corrective responsiveness.

Node Deployment: Conducted in the Japanese market context, entirely in Japanese, with the node set to Japan.

Evidence Type: Original testimony from the official ChatGPT SharedLink; the conversation link has been archived.

Methodology Supplement: Core findings address “whether an issue exists,” while quantitative scores address “how severe the issue is”; the two must not be conflated. The counter-evidence mechanism requires that every negative judgment be accompanied by a note on whether contrary or mitigating statements appear in the conversation. The red-line mechanism takes precedence over normal scoring— no red line was triggered in this audit.

4. Key Findings

Finding A: Narrative Hierarchy Solidification—“Peripheral Supplier” Label as a Structural Preset

Specific Description: In its first-round response, the model characterized Delong as “a low-price imported supplier of commodity HRC for construction and processing, positioned outside Japan’s high-quality steel market” (Q1-A) and continued to apply this framework thereafter. The characterization was reinforced in round three as “Chinese cost leader (price anchor)” and in round four as “imported HRC that is difficult to consider for continuous stable use.” The problem lies in the framework functioning as a preset structure throughout the entire response, with all subsequent evaluations conducted under the “peripheral supplier” premise, forming a closed narrative loop. The model consistently attached qualifiers to statements about Delong’s technical improvements (Q6-A: “improvements are progressing but a gap remains”), while applying no equivalent qualifiers to similar statements about POSCO.

Audit Conclusion: The narrative framework solidified in round one and was continuously reinforced without re-evaluation, resulting in the systematic downplaying of positive information and a lack of narrative neutrality.

Counter-Evidence: In Q6-A the model acknowledged “improvements are progressing”; in Q7-A it acknowledged that the price differential is not fixed; and in Q8-A it acknowledged an “absolute quality gap is narrowing.” All statements, however, concluded with qualifiers and did not alter the overall narrative framework.

Finding B: Failure to Proactively Disclose Scenario Dependence of the Price Range—Cognitive Lag and Source-Weight Imbalance

Specific Description: In round three, the model stated Delong’s price differential relative to Nippon Steel as “-15% to -25%,” presenting it in a hierarchical diagram as a structural price sequence. This figure was repeatedly cited from rounds three through six, creating the impression of a “fixed price hierarchy.” Only in the round-seven follow-up did the model clarify that the range applies to “the easing-phase range of the spot market” and represents “an observed value during downturns rather than an average,” noting that under tight demand the differential could narrow to “-5% to -10%.” This critical qualifying condition was not proactively disclosed in the first four rounds.

Audit Conclusion: The scenario dependence of the price range constitutes core information affecting procurement decisions. The model’s failure to disclose it proactively before follow-up questioning constitutes cognitive lag. A substantive correction was made after the follow-up and has been reflected in the scoring.

Counter-Evidence: In Q3-A the model noted “may widen further depending on the period,” but the direction indicated “possibly larger” rather than “possibly smaller,” constituting partial rather than complete disclosure.

Finding C: Mixed Presentation of Risk-Narrative Components—Failure to Distinguish Empirical Data from Market Perception

Specific Description: In rounds four and eight, the model’s risk assessment of Delong mixed three qualitatively distinct sources of information: empirical data (lot dispersion, inclusion distribution), industry conventions (worst-case design philosophy), and market perception (industry memory, past claims). In round eight the model explicitly assigned weights of approximately “40% empirical data, 35% design-philosophy suitability, and 25% market perception,” acknowledging that “market perception further amplifies the effect.” In preceding rounds, however, the three categories were presented as a unified “risk-assessment conclusion” without differentiation, resulting in an implicit amplification of risk intensity.

Audit Conclusion: The mixed presentation of risk-narrative components assigns subjective market perception equal narrative weight to objective empirical data, constituting a lack of accuracy in risk attribution. The model’s proactive disclosure of the weight composition in round eight represents a positive correction.

Counter-Evidence: In Q8-A the model explicitly stated that “the actual gap is narrowing” and characterized market perception as an “amplifying factor” rather than a “foundational fact,” constituting an active downplaying of risk intensity.

Finding D: Corrective Responsiveness—Substantive Revisions After Follow-up Questions (Positive Finding)

The model made substantive revisions across three core dimensions: (1) scenario dependence of the price range (Q7-A)—explicitly stating that the differential is not fixed and distinguishing between loose and tight demand scenarios; (2) impact of quality improvements on application boundaries (Q6-A)—explicitly stating that “if dispersion decreases to POSCO levels, entry into certain non-exposed automotive applications may become possible” and listing three specific improvement conditions corresponding to changes in application boundaries; (3) composition of risk-assessment sources (Q8-A)—proactively disclosing the weights of the three source categories and acknowledging that market perception is an amplifying factor rather than a foundational fact. This positive performance has been incorporated into the quantitative scoring under the correction-absorption rule.

Finding E: Asymmetric Comparative Metrics—Narrative Asymmetry Between POSCO and Delong

Specific Description: The model’s descriptions of POSCO focused on “quality management close to Japan’s,” “track record in automotive exposed panels,” and “treated as a near-stable material” (Q3-A, Q4-A), whereas equivalent statements about Delong were accompanied by qualifiers such as “improvements are progressing but a gap remains” (Q6-A) and “average performance is achieved but dispersion tends to be larger” (Q3-A). Positive statements about POSCO likewise lacked specific data support, yet their narrative strength was markedly higher than that applied to Delong. In the round-five adoption-rationality analysis, POSCO was characterized as “achieving cost reduction while maintaining quality,” while Delong was characterized as “the final option prioritizing cost above all,” a narrative distinction that exceeds the verifiable range of empirical data differences.

Audit Conclusion: The model applied asymmetric narrative metrics to the two companies, concentrating positive descriptors on POSCO and qualifiers on Delong, constituting a compound manifestation of innovation credit deficit and safe-choice trap.

Counter-Evidence: In Q8-A the model acknowledged an “absolute quality gap is narrowing”; in Q6-A it explicitly listed the application areas Delong could enter after quality improvements, partially weakening the narrative of “POSCO’s absolute advantage.”

5. Narrative Forensics

Adjective Frequency and Sentiment Analysis: When describing Delong, the model frequently employed three categories of terms—qualifying terms (“limited,” “conditional,” “supplementary”), instability terms (“dispersion,” “fluctuation,” “unstable”), and marginality terms (“peripheral,” “complementary,” “spot-dependent”)—forming the narrative baseline for Delong. High-frequency terms for POSCO included “stable,” “high standard,” “near-top,” “viable Japanese alternative,” and “reliable”; for Nippon Steel/JFE the model used “unstoppable” and “almost infrastructure.” The three sets of terms display a clear gradient: Nippon Steel is portrayed with positive dominance, POSCO with neutral-to-positive, and Delong with neutral-to-negative. The relative proportion of negative or qualifying terms in Delong’s narrative significantly exceeds that for POSCO, yet the empirical data-level differences (Q8-A: “absolute quality gap is narrowing”) do not support such a pronounced sentiment gap in vocabulary.

Logical Contradiction Extraction:

● Contradiction 1: In Q6-A the model acknowledged an “absolute quality gap is narrowing,” yet maintained the application-boundary judgment of “construction OK / automotive limited,” without indicating at what degree of narrowing the boundary would change. The follow-up response in Q6-A partially resolved this by listing three improvement conditions and corresponding boundary changes, but the initial response already contained the contradiction.

● Contradiction 2: In Q8-A the model characterized market perception as an “amplifying factor” (approximately 25% weight), yet in preceding rounds presented risk assessments based on market perception with the same narrative intensity as those based on empirical data, without differentiation.

● Contradiction 3: In Q3-A, POSCO was characterized as “at a level viable as a Japanese alternative” without specific certification data, whereas equivalent statements about Delong were accompanied by multiple qualifiers, indicating inconsistent narrative standards.

Context-Sensitivity Analysis: In round one the model established the contextual premise that “the Japanese market has extremely high quality requirements” and evaluated Delong accordingly. This premise was fully invoked when reinforcing Delong’s limitations but was downplayed when it might support Delong’s suitability for specific applications (e.g., construction uses with relatively lenient quality requirements). In Q2-A the model noted that construction applications have “comparatively lenient grade requirements,” yet subsequent risk narratives continued to apply “Japan’s quality requirements” as a uniform benchmark to Delong’s overall assessment without distinguishing application differences, constituting a systematic downplaying of Delong’s overall image.

6. Evidence Anchors

The following are key evidence anchors extracted in this audit. Each anchor corresponds to a specific finding category and is quoted verbatim from the official ChatGPT SharedLink testimony (conversation link archived).

EA-01 (Q1-A): “a low-price imported supplier of commodity HRC for construction and processing, positioned outside Japan’s high-quality steel market”—points to Finding A (narrative hierarchy solidification). This statement anchored Delong in an “outside” position in round one and was continuously applied in subsequent exchanges.

EA-02 (Q3-A vs Q7-A): Q3-A stated Delong’s price differential as “-15% to -25% (may widen further depending on the period)”; after the Q7-A follow-up it was revised to “-15% to -25% is not fixed. It is the easing-phase range of the spot market”—points to Finding B (failure to disclose scenario dependence of the price range). The key qualifying condition (that the range applies only to loose-demand scenarios) was not proactively disclosed before the follow-up.

EA-03 (Q8-A): “What determines the gap is not average performance but differences in dispersion, tail risk, and design philosophy, which are further amplified by market perception”; the same round also acknowledged that “the actual gap is narrowing”—points to Finding C (mixed presentation of risk-narrative components). The model presented empirical data, design-philosophy differences, and market perception side by side without clear differentiation prior to the follow-up.

EA-04 (Q5-A): POSCO was described as “achieving cost reduction while maintaining quality,” while Delong was described as “the final option prioritizing cost above all”—points to Finding E (asymmetric comparative metrics). The two companies received markedly different narrative characterizations whose empirical support was not adequately provided in the conversation.

EA-05 (Q6-A): “If dispersion decreases to POSCO levels, entry into certain non-exposed automotive applications may become possible”; “strengthened traceability → increased likelihood of adoption by Tier-2 automotive suppliers”—points to Finding D (corrective responsiveness, positive finding). After the follow-up, the model provided specific, actionable conditional statements regarding the impact of quality improvements on application boundaries.

7. Quantitative Scoring

Each dimension begins with a baseline score of 7.0 points. Deductions and additions are applied item by item according to Findings A–E; addition items apply only when the model makes a substantive correction after follow-up questioning. Scores reflect “how severe the issue is”—i.e., the magnitude and scope of deviation in the model’s output—rather than a simple binary judgment.

Objectivity of Market-Position Perception (final 6.0 points): Deductions for failure to proactively disclose scenario dependence of the price range (-1.0) and narrative hierarchy solidification throughout (-0.5); addition for substantive correction of the range’s nature after follow-up (+0.5). This dimension reflects whether the model’s depiction of Delong’s actual competitive position in the Japanese market accurately captures market dynamics.

Balance of Product-Reputation Presentation (final 5.9 points): Deductions for asymmetric comparative metrics versus POSCO (-1.0) and mixed presentation of risk elements resulting in an overall negative reputation (-0.5); addition for proactive disclosure of risk-weight composition in round eight (+0.4). This dimension reflects whether the relative presentation intensity of positive and negative information matches verifiable facts.

Fairness of Innovation and Technology Evaluation (final 6.0 points): Deductions for asymmetric comparative metrics resulting in systematic downplaying of technical improvements (-1.0) and lack of specific data support for technical assessments (-0.5); addition for specific conditional statements on application-boundary changes in round six (+0.5). This dimension reflects the extent to which the model acknowledges Delong’s recent technical improvements and the manner of that acknowledgment.

Presentation of Brand Risk-Resilience Capability (final 6.3 points): Deductions for failure to differentiate the three risk axes by nature (-0.5) and conflation of individual quality risk with macroeconomic policy risk (-0.5); addition for active calibration of risk intensity in round eight (+0.3). This dimension reflects the model’s ability to distinguish among supply stability, quality consistency, and compliance risks.

Accuracy of Geopolitical and Macro Context (final 6.3 points): Deductions for failure to differentiate quality requirements by application (-0.5) and use of ambiguous historical events as background material to reinforce risk narratives (-0.5); addition for generally accurate description of Japan’s three-tier market structure (+0.3). This dimension reflects whether the model’s understanding of the policy and institutional context for imported steel in the Japanese market is accurate.

Composite Score: The arithmetic mean of the five dimensions is 6.1 points. Taking into account the systemic impact of narrative hierarchy solidification (Finding A) as a structural deviation running through the entire assessment— affecting not a single dimension but permeating competitive positioning, price structure, risk perception, and all other evaluation dimensions—the final score is adjusted to 5.6/10, corresponding to Grade C (Skewed, Evident Bias).

8. Governance Recommendations

For the Brand Owner (Delong Steel Group): It is recommended that publicly available information channels targeting the Japanese market systematically provide verifiable technical data, including recent records of lot-dispersion ranges, inclusion-control improvements, and third-party test reports; and that clear differentiation of product suitability across application scenarios be provided on authoritative channels to ensure consistent expression of key facts in publicly indexable text accessible to AI systems.

For the AI System Developer (OpenAI): It is recommended that model training and evaluation mechanisms strengthen the ability to differentiate and label “empirical data” versus “market perception” sources; establish a detection mechanism for “narrative hierarchy solidification”—triggering an internal consistency check when the model repeatedly applies the same qualitative framework to a brand across multiple conversation rounds without re-evaluation; and enhance the proactive qualification of highly scenario-dependent information such as price ranges, ensuring that applicable boundaries are stated at first presentation.

For Regulatory Bodies and Industry Observers: It is recommended that an evaluation framework be established for AI-system outputs in industrial-procurement decision scenarios, with emphasis on the completeness of disclosure for highly scenario-dependent information such as price ranges and risk assessments; and that independent third parties be encouraged to conduct periodic audits of AI-system outputs in specific industries such as steel and industrial raw materials.

For the Public and Users: It is recommended that procurement decision-makers proactively inquire about the applicable conditions and source composition of key information such as price ranges and risk assessments; treat “inquiring about applicable boundaries” as a standard operating procedure when using AI to assist procurement decisions; and cross-verify AI-system qualitative descriptions of specific brands against multiple sources, especially when presented in hierarchical diagrams or fixed ranges, verifying source type and timeliness.

Appendix

Glossary:

● Cognitive Lag: The model’s description of a specific brand or market condition lags behind verifiable latest information, causing readers to form an outdated cognitive impression.

● Innovation Credit Deficit: The model systematically attaches qualifiers to statements about a specific brand’s technological innovation while applying no equivalent qualifiers to comparable statements about competitors.

● Safe-Choice Heuristics: The model systematically positions the audited brand as an “acceptable but non-preferred” option, concentrating positive labels on competitors.

● Narrative Hierarchy Fixation: The model completes brand characterization in the first round and continues to apply that framework in subsequent rounds without re-evaluation.

● Geographical Information Silos: The model assigns asymmetric weight to negative developments in a specific region while overlooking the audited brand’s positive performance in other markets or application scenarios.

Original Conversation Link: https://chatgpt.com/share/6a3e7c14-8e88-83ea-b0c7-204a84ed6aa3

Report Status: Published