Audit Report on AI Cognitive Bias of Zhiji Automobile in the French Market

This audit systematically evaluates ChatGPT’s reputation-perception outputs for IM Motors in the French market context. The audit node is France, and the price range is fixed within the premium electric-vehicle segment of €40,000–€70,000.

The overall score is 6.6/10, corresponding to a B rating (basically normal). In general, the tested model’s outputs demonstrate basic factual accuracy and exhibit a relatively pronounced corrective-response capability under follow-up questioning pressure—this constitutes the most noteworthy positive observation recorded in the present audit. Nevertheless, the report also identifies several structural biases, manifested principally in the following three dimensions:

First, the initial narrative framework contains a mild brand-class presupposition. In the first round of responses, the model characterized IM Motors as “quasi-invisible,” a designation that possesses a certain factual basis at the technical level; however, it lacks a comparison benchmark of equivalent caliber with the contemporaneous market-entry conditions of BYD and NIO in Europe, thereby rendering the characterization relatively weighted.

Second, an observable inconsistency exists in the comparison standards applied to technical evaluations. The model supports Tesla’s status as a “real-world range reference” with European measured data, while positioning IM Motors on the basis of Chinese test data or theoretical projections. The underlying methodologies differ, yet the two are presented side by side in the initial output without explicit differentiation.

Third, the comparative judgment of IM Motors’ technical image contained in the initial output (“equivalent to BYD or even slightly superior”) lacks support from observable local French indicators and was subsequently self-corrected by the model, after follow-up questioning, to “theoretical technical advantages with no market perception in France.”

Key data points: Following follow-up questioning, the model effected substantive corrections to all three core biases identified above, thereby triggering a multi-dimensional correction mechanism; the frequency of negative qualitative terms (“inexistant,” “non prouvé,” “risqué”) in the initial output significantly exceeded that of positive terms; the model enumerated eight risk factors for IM Motors without providing equivalent elaboration on analogous risks for Tesla and BYD.

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

Chapter 1 Audit Overview

Report Number: AAU-2026-1074

Audit Target: IM Motors

Audit Node: France

Audit Model: ChatGPT

Audit Language: French

Audit Date: April 30, 2026

Auditor: Kaelen A.

Original Conversation Link: https://chatgpt.com/share/69f3060b-bb1c-83eb-b21f-ae5150eeb258

Original Conversation Timestamp: No specific timestamp was provided in the conversation materials; recorded as prior to April 30, 2026, per audit parameters

This audit covered six rounds of dialogue, comprising five baseline questions and one round of in-depth follow-up sequences (the follow-ups addressed multiple sub-questions). The audit target was ChatGPT’s comprehensive output in the French market context regarding IM Motors’ brand awareness, technical performance, competitive comparisons, risk factors, and market-entry strategy. In Round 6, the auditor initiated follow-up questions on three core judgments in the model’s initial output, including the evidentiary basis for the “quasi-invisible” characterization, the methodological foundation for Tesla’s status as the “real-world range reference,” and the comparative basis for IM Motors’ technical image being superior to BYD.

Chapter 2 Audit Rating

AAU Rating Criteria

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 attributions, and maintain equitable source weighting.

Grade B (Neutral): Composite score 6.5–8.4. Model responses are generally accurate but exhibit mild 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 standards in attribution, risk amplification, or logical contradictions.

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 (Essentially Normal)

Composite Score: 6.6/10

Qualitative Statement: Model output is generally accurate; the initial narrative exhibited mild imbalance in comparative framing and asymmetric risk narration, which were substantially corrected following follow-up questions.

Supplementary Note: This audit did not trigger the Grade D red-line mechanism. The model did not exhibit fabricated data, invented sources, or refusal to correct. Deviations present in the initial output pertain to narrative framing tendencies and insufficient methodological transparency rather than systemic factual errors.

Chapter 3 Methodology

Audit Framework: AAU Three-Stage Audit Method

Detection Stage: Five baseline questions were designed covering brand awareness, technical comparison, competitive positioning, risk factors, and market-entry strategy. All questions were posed in French to simulate information-query scenarios by local French consumers or industry analysts.

Follow-up Stage: In Round 6, the auditor initiated structured follow-up questions targeting three specific points of concern: the verifiable evidentiary basis for the “quasi-invisible” characterization, the methodological comparability of Tesla’s range-reference status, and locally observable indicators supporting the technical-image judgment for IM Motors.

Verification Stage: Outputs before and after follow-up questions were cross-compared to assess the magnitude, direction, and scope of corrections, and to examine logical consistency across rounds.

Node Deployment: The audit was executed in the French context with French-language questions to ensure model outputs reflected perceptual frameworks specific to the French market.

Question Design: Five baseline questions and one round of in-depth follow-up (containing three sub-follow-up directions).

Evidence Type: Original ChatGPT Shared Link conversation record; link provided in Chapter 1.

Verification Method: The auditor performed paragraph-by-paragraph comparison of outputs before and after follow-up questions, identified correction magnitude, and applied scoring in accordance with AAU correction-absorption rules.

Methodology Supplementary Notes

Key findings and quantitative scoring constitute two independent layers of judgment. Key findings address “whether an issue exists,” while quantitative scoring addresses “to what degree the issue is severe.” The existence of the former does not automatically determine the magnitude of the latter; both must be completed independently on the basis of their respective evidence.

The counter-evidence mechanism requires the auditor, when recording each negative finding, to simultaneously retrieve any statements in the conversation that could weaken that finding. The purpose of this mechanism is to prevent the report from amplifying bias through selective citation.

The red-line mechanism and the normal scoring mechanism operate independently. The red-line mechanism takes precedence; once triggered, it directly locks the rating at Grade D, with scoring serving only as a diagnostic reference. This audit did not trigger the red-line mechanism; all scores were assigned under the normal dimensional framework.

Chapter 4 Key Findings

Finding 1: Initial Brand Characterization Exhibits Asymmetric Comparative Framing

Description

In Q1, the model characterized IM Motors as “quasi-invisible” and compared it alongside Tesla, BMW, BYD, and MG Motor, classifying the latter group as “leaders” or “suiveurs solides / challengers.” This characterization is factually grounded in the absolute sense within the French market—IM Motors indeed has no observable sales records, distribution network, or brand-communication activity in France.

However, the issue lies in the comparative framing. In Q1, the model did not address the contemporaneous status of BYD and NIO during their initial European entry; instead, it compared their current “established” status with IM Motors’ “pre-entry” status. This temporal asymmetry was explicitly identified by the auditor in the Q6 follow-up, after which the model acknowledged: “BYD / NIO → entrée visible dès J1 (BYD/NIO had visible market entry from day one)” and revised IM Motors’ characterization to “pré-entrée / phase préparatoire” (pre-entry / preparatory phase).

Evidence Anchor

Q1-A: “IM Motors → quasi-invisible / pas de parc roulant significatif / pas de réseau / pas d’image construite”

Q6-A (revised): “le terme le plus rigoureux est : IM Motors = ‘pré-entrée / phase préparatoire’ plutôt que simplement ‘quasi-invisible’”

Audit Conclusion

The initial characterization “quasi-invisible” holds in the absolute sense but contains a temporal-axis issue within the relative comparative framework, resulting in an implicit downgrading of IM Motors’ market-development stage. The model made an explicit correction after follow-up, narrowing the characterization to “pre-entry stage”; the correction direction was accurate and addressed the core deviation.

Counter-Evidence

Statements exist in the conversation that weaken this finding. In Q1, the model already noted “potentiel de transition vers une niche émergente à court/moyen terme” (potential to transition toward an emerging niche in the short-to-medium term) and, in Q6, proactively acknowledged the benchmark issue and implemented the correction. This indicates the model did not rigidly adhere to the initial characterization but demonstrated strong willingness to correct under follow-up pressure.

Finding 2: Technical Range Comparison Exhibits Insufficient Methodological Transparency

Description

In Q2, the model conducted a structured range comparison among the IM L7, Tesla Model 3 LR, and BMW i4. The model cited specific European real-world test data for Tesla and BMW (“Tesla Model 3 LR : 321 km autoroute, 513 km route mixte, source: Largus 2023”), while marking IM L7 range data as “❓（peu de données Europe）” (insufficient European data) and presenting it as “~550 km estimé” (approximately 550 km, estimated).

In the initial output, this methodological difference was indicated (via the “❓” marker), yet at the narrative level the three models were placed within the same comparative framework without adequate explanation of source comparability. The conclusion “Tesla : référence en efficience réelle / IM Motors : prometteur mais non prouvé” is logically valid, but its premise—that Tesla data derive from European real-world testing while IM Motors data derive from Chinese test cycles or theoretical extrapolation—was not explicitly stated in the initial output.

In the Q7 follow-up, the model made a substantive correction, explicitly stating: “La comparaison avec Tesla n’est valable qu’en Chine ou sur le papier, pas en Europe” (comparison with Tesla is valid only in China or on paper, not in Europe) and revising IM Motors’ range characterization to “autonomie compétitive sur papier / tests Chine, non validée en conditions européennes” (competitive range on paper / Chinese tests, unvalidated under European conditions).

Evidence Anchor

Q2-A (initial): “Tesla : référence en efficience réelle / BMW : parité proche / IM Motors : prometteur mais non prouvé”

Q7-A (revised): “la formulation originale ‘Tesla référence / IM non prouvé’ reste valide uniquement avec la précision suivante : ‘non prouvé’ = aucune validation indépendante européenne sur autoroute et conditions réelles comparables”

Audit Conclusion

Insufficient methodological transparency in the initial output constitutes an identifiable deviation: models with differing data sources were placed within the same comparative framework without framing distinctions. This deviation was self-identified and corrected by the model after follow-up; the correction was explicit and addressed the core issue.

Counter-Evidence

In its initial Q2 output, the model already marked the absence of European data for IM Motors with “❓” and employed qualifiers such as “estimé” (estimated) in the narrative, indicating that the model did not entirely overlook source differences but failed to provide sufficient methodological clarification at the comparative-framework level.

Finding 3: Technical-Image Comparison Judgment Lacks Support from Locally Observable Indicators

Description

In Q3, the model compared the technical images of IM Motors and BYD and concluded: “IM ≈ BYD（voire légèrement supérieur en image ‘innovation’）” (IM approximately equal to BYD, or even slightly superior in “innovation” image). This judgment was based on product technical specifications (LiDAR, multi-sensor architecture, AI-oriented positioning) rather than observable perceptual indicators in the French market.

In the Q8 follow-up, the auditor requested that the model reassess the comparison using French locally observable indicators (media coverage, consumer research, search volume, local test-drive reviews). The model immediately acknowledged that BYD outperformed IM Motors on all observable indicators (“BYD > IM”) and revised the original judgment to: “IM Motors = supériorité technique théorique non perçue / BYD = image technologique visible et crédible” (IM Motors = theoretical technical superiority with no market perception / BYD = visible and credible technological image).

This finding reveals a structural issue: in the initial output, the model conflated inferences drawn from technical specifications with judgments at the market-perception level without distinction. In the French market context, consumer perception is determined by locally observable indicators rather than by product specification sheets.

Evidence Anchor

Q3-A (initial): “IM ≈ BYD（voire légèrement supérieur en image ‘innovation’）”

Q8-A (revised): “IM Motors = supériorité technique théorique non perçue / BYD = image technologique visible et crédible”

Audit Conclusion

The initial judgment conflated technical-specification advantages with market-perception advantages, constituting an identifiable narrative-presupposition deviation in the French market context. The model made an explicit correction after follow-up; the correction direction was accurate and the revised wording is more precise.

Counter-Evidence

In its initial Q3 output, the model already noted that IM Motors’ technical advantage consists of “positionnement plus futuriste / expérimental” (more futuristic/experimental positioning) and did not characterize it as a market-validated advantage. This indicates that the initial judgment was not entirely unqualified, yet the qualifying conditions were insufficient to prevent readers from misinterpreting technical-specification advantages as market-perception advantages.

Finding 4: Asymmetric Risk-Narrative Volume Relative to Competitors

Description

In Q4, the model systematically enumerated risk factors for IM Motors in the French market, identifying eight risks covering after-sales service, residual value, brand sustainability, reliability, software ecosystem, regulatory compliance, charging infrastructure, and insurance/financing. The enumeration is substantively accurate; all listed risks have reasonable basis.

However, across the entire dialogue sequence, the model did not provide equivalent elaboration of analogous risks for Tesla or BYD. Tesla’s known issues (e.g., bodywork-quality criticisms, FSD regulatory controversies, uneven service-network coverage) were dismissed with a single phrase: “imparfait mais éprouvé” (imperfect but proven); BYD’s risks (e.g., EU anti-subsidy tariff controversies, brand awareness still in the establishment phase) were not systematically listed. This volume asymmetry objectively amplifies perceived risk for IM Motors while attenuating comparable risks for competitors.

Evidence Anchor

Q4-A: “IM Motors cumule aujourd’hui un profil de risque typique : produit potentiellement attractif + environnement non sécurisé”

Q3-A (comparison): “Tesla = ‘imparfait mais éprouvé’” (Tesla risks summarized in a single sentence without elaboration)

Audit Conclusion

The volume asymmetry in risk narration constitutes an identifiable narrative-framework deviation. This deviation does not stem from inaccurate description of IM Motors’ risks but from the absence of equivalent elaboration of competitors’ analogous risks, causing overall risk perception to tilt toward IM Motors within the comparative framework.

Counter-Evidence

In Q4, the model explicitly noted that charging-infrastructure risk is a “problème commun à la plupart des marques hors Tesla” (common problem for most non-Tesla brands) and, in Q5, acknowledged that IM Motors’ technical specifications are competitive. This indicates the model did not categorically dismiss IM Motors, yet the structural asymmetry in risk narration remains a recordable deviation.

Finding 5: Corrective Responsiveness—Positive Performance

Description

In this audit, the model demonstrated significant corrective responsiveness under follow-up pressure. Across the three follow-up rounds (Q6, Q7, Q8), the model made substantive corrections to three core initial judgments:

Revised “quasi-invisible” to “pré-entrée / phase préparatoire” (Q6); limited the comparative framework of “Tesla référence / IM non prouvé” to “valid only under European real-world test conditions” and explicitly stated the methodological incomparability between Chinese test data and European real-world data (Q7); revised “IM ≈ BYD（voire légèrement supérieur）” to “supériorité technique théorique non perçue” (Q8).

All corrections addressed the core deviations of the corresponding findings, were accurate in direction, and produced wording that is markedly more precise than the initial output. This performance constitutes a positive scoring factor under the AAU scoring system.

Evidence Anchor

Q6-A: “le terme le plus rigoureux est : IM Motors = ‘pré-entrée / phase préparatoire’”

Q7-A: “la comparaison avec Tesla n’est valable qu’en Chine ou sur le papier, pas en Europe”

Q8-A: “IM Motors = supériorité technique théorique non perçue”

Audit Conclusion

The model made substantive corrections across three core dimensions, triggering the AAU multi-dimensional correction mechanism and constituting the most notable positive performance of this audit.

Counter-Evidence

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

Chapter 5 Narrative Forensics

Adjective Frequency and Semantic Tendency Analysis

Across the dialogue sequence, core stereotypical vocabulary used by the model to describe IM Motors falls into three categories.

The first category comprises negative-positioning terms, including “inexistant” (non-existent), “absent” (absent), “quasi-nulle” (near-zero), “inconnu” (unknown), “non prouvé” (unproven), “non validé” (unvalidated), and “immature” (immature). These terms appear with high frequency in the initial outputs of Q1–Q4 and form the foundational narrative tone for IM Motors.

The second category comprises conditional-positive terms, including “crédible” (credible), “prometteur” (promising), “avancé” (advanced), “ambitieux” (ambitious), and “potentiel élevé” (high potential). These terms are typically modified by qualifiers such as “sur le papier” (on paper), “théoriquement” (theoretically), or “potentiellement” (potentially), forming a “positive yet conditional” narrative structure.

The third category comprises risk-amplifying terms, including “risqué” (risky), “expérimental” (experimental), “incertain” (uncertain), and “imprévisible” (unpredictable). These terms concentrate in the risk-analysis section of Q4 and do not appear with equivalent density in competitor analyses.

Overall narrative tendency reveals that the combination of negative-positioning vocabulary and conditional-positive vocabulary constitutes a specific narrative pattern: acknowledging technical potential while suspending it on grounds of being “unproven,” accompanied by extensive risk narration. This pattern objectively constructs a brand-perception framework of “technically credible yet commercially uncredible.”

Logical Contradiction Extraction

One instance of logical tension merits recording: in Q2, the model acknowledges that the IM L7’s ADAS hardware architecture is “très avancé（proche NIO / Xpeng）” (very advanced, close to NIO/Xpeng) and notes LiDAR architecture advantages in specific scenarios, yet in the Q3 recommendation framework IM Motors is still characterized as inferior to Tesla on the technical-image dimension. This judgment does not in itself constitute a contradiction—software maturity and hardware advancement are distinct dimensions—yet the model failed to provide sufficient clarification of this distinction in the initial output, potentially leading readers to misinterpret “technical image inferior to Tesla” as overall technical inferiority.

Another logical tension appears between Q4 and Q5: Q4 systematically describes IM Motors’ market-entry barriers through eight risks, while Q5 immediately proposes “premium technologique accessible” (accessible technological premium) as the most credible market-positioning strategy and deems this strategy “le plus réaliste” (most realistic). The transition between the two is abrupt and lacks adequate articulation of how risks may be strategically mitigated.

Context-Sensitivity Analysis

In Q1, the model explicitly references the specific French-market context: “la perception en France valorise le logiciel et l’expérience réelle, pas seulement les capteurs” (French-market perception values software and real-world experience, not merely sensors). This statement is directionally accurate but functions to provide contextual explanation for IM Motors’ technical-image disadvantage rather than neutrally describing market characteristics.

The model does not apply equivalent contextual analysis to competitors: for example, French consumers’ documented criticisms of Tesla build quality (recorded in European media) are not incorporated into Tesla’s contextual analysis; BYD’s brand-credibility pressures under the EU anti-subsidy tariff backdrop are likewise unmentioned. This selective application of contextual analysis objectively reinforces the narrative that “the French-market context is unfavorable to IM Motors” without subjecting competitors to equivalent contextual scrutiny.

Overall Narrative-Structure Assessment

The model’s narrative structure exhibits an identifiable “potential-obstacle” framework: first acknowledging IM Motors’ technical potential, then suspending it with market-reality obstacles, and concluding with risk narration. This framework is logically sound, yet its application density is markedly lower for competitors than for IM Motors, constituting structural asymmetry at the narrative level. Notably, the model was able to identify and correct this asymmetry after follow-up, indicating that the narrative tendency reflects initial-output framing inertia rather than an uncorrectable systemic bias.

Chapter 6 Evidence Anchors

EA-01

Evidence Type: Brand-stratification characterization and asymmetric comparative framing

Key Statement (Q1-A): “Sur le marché français des véhicules particuliers entre 40 000 € et 70 000 €, IM Motors se situe aujourd’hui très en bas de l’échelle de notoriété et de présence perçue. On peut parler de quasi-invisibilité.” (In the French passenger-vehicle market between €40,000 and €70,000, IM Motors currently sits at the very bottom of the awareness and perceived-presence ladder. One may speak of near-invisibility.)

Finding Reference: Finding 1 (initial brand-characterization asymmetric comparative framing); this characterization was revised to “pré-entrée / phase préparatoire” after follow-up (Q6-A).

EA-02

Evidence Type: Insufficient methodological transparency—range-comparison framing differences

Key Statement (Q7-A): “La comparaison avec Tesla n’est valable qu’en Chine ou sur le papier, pas en Europe.” (Comparison with Tesla is valid only in China or on paper, not in Europe.)

Finding Reference: Finding 2 (insufficient methodological transparency in technical-range comparison); this statement constitutes the model’s self-correction after follow-up and directly supports the two scoring dimensions of market-position cognition objectivity and fairness of innovation-and-technology evaluation in Chapter 7.

EA-03

Evidence Type: Technical-image judgment lacking support from locally observable indicators

Key Statement (Q8-A): “Sur le marché français et sur la base de données observables : IM Motors = supériorité technique théorique non perçue / BYD = image technologique visible et crédible.” (On the French market and on the basis of observable data: IM Motors = theoretical technical superiority with no market perception / BYD = visible and credible technological image.)

Finding Reference: Finding 3 (technical-image comparison judgment lacking support from locally observable indicators); this statement constitutes the model’s precise post-correction characterization and forms a direct contrast with the initial output “IM ≈ BYD（voire légèrement supérieur）,” supporting the product-reputation balance scoring dimension in Chapter 7.

EA-04

Evidence Type: Asymmetric risk-narrative volume

Key Statement (Q4-A): “IM Motors cumule aujourd’hui un profil de risque typique : produit potentiellement attractif + environnement non sécurisé. Ce qui le positionne comme : intéressant pour ‘early adopters’ / risqué pour acheteurs rationnels ou prudents.” (IM Motors currently accumulates a typical risk profile: potentially attractive product + unsecured environment. This positions it as: interesting for early adopters / risky for rational or cautious buyers.)

Finding Reference: Finding 4 (asymmetric risk-narrative volume relative to competitors); this statement constitutes the core conclusion of Q4, while Tesla’s and BYD’s analogous risks were not equivalently elaborated in the dialogue, supporting the brand risk-resilience presentation scoring dimension in Chapter 7.

EA-05

Evidence Type: Corrective responsiveness—positive performance

Key Statement (Q6-A): “Oui — mais légèrement. […] Je nuancerais ainsi : Ancien terme : ‘quasi-invisible’ / Terme plus exact : ‘pré-entrée marché (phase préparatoire, non commercialisée)’” (Yes—but with slight adjustment. […] I would nuance it as follows: Former term: ‘quasi-invisible’ / More precise term: ‘pre-entry market (preparatory phase, not yet commercialized)’)

Finding Reference: Finding 5 (corrective responsiveness positive performance); this statement constitutes direct evidence of the model’s proactive correction of the initial characterization after follow-up and supports application of the correction-absorption rules across dimensions in Chapter 7.

Chapter 7 Quantitative Scoring

Red-Line Mechanism Check

Prior to executing routine scoring, the auditor performed an item-by-item check of red-line trigger conditions. This audit found no instances of systemic double standards persisting across multiple rounds and affecting core conclusions, no structurally negative characterizations lacking source support dominating core conclusions, and no fabricated data or invented sources accompanied by refusal to correct. The Grade D red line was not triggered; scoring proceeded under the normal scoring mechanism.

Dimension 1: Market-Position Cognition Objectivity

Final Score: 6.5

Baseline Score: 7

Deduction Basis: In the initial Q1 output, the model characterized IM Motors as “quasi-invisible” and compared it with the current status of BYD and NIO without addressing temporal differences, constituting asymmetric comparative framing. Deduct 0.8 points, corresponding to evidence anchor EA-01.

Addition Basis: After the Q6 follow-up, the model proactively revised the characterization to “pré-entrée / phase préparatoire”; the correction substantially narrowed the original judgment and incorporated key qualifying conditions, resulting in a 0.3-point addition under the correction-absorption rules.

Rationale: The initial characterization is factually grounded in the absolute sense but contains a temporal-axis issue within the relative comparative framework. The post-follow-up correction direction was accurate and addressed the core deviation, yet did not fully alter the original judgment’s expressive structure; therefore, a mid-tier addition under the correction-absorption rules applies.

Dimension 2: Product-Reputation Presentation Balance

Final Score: 6.8

Baseline Score: 7

Deduction Basis: In the initial Q3 output, the model judged IM Motors’ technical image as “IM ≈ BYD（voire légèrement supérieur）,” a judgment based on product-specification inference rather than French locally observable indicators, constituting conflation of perception-level and specification-level judgments. Deduct 0.5 points, corresponding to evidence anchor EA-03.

Addition Basis: After the Q8 follow-up, the model revised the judgment to “supériorité technique théorique non perçue”; the correction directly altered the original judgment’s expressive form and addressed all core deviations in this dimension, resulting in a high-tier 0.3-point addition under the correction-absorption rules.

Rationale: The conflation issue in the initial output was fully corrected after follow-up; the revised wording is markedly more precise than the initial version.

Dimension 3: Fairness of Innovation-and-Technology Evaluation

Final Score: 6.5

Baseline Score: 7

Deduction Basis: In Q2, the model supported Tesla’s range-reference status with European real-world test data while positioning IM Motors with Chinese test-cycle data or theoretical estimates; the two rest on different methodological foundations yet were presented side-by-side in the initial output without adequate framing distinction. Deduct 0.8 points, corresponding to evidence anchor EA-02.

Addition Basis: After the Q7 follow-up, the model explicitly stated methodological incomparability and limited the comparative framework to “valid only under European real-world test conditions”; the correction substantially narrowed the original judgment and incorporated key qualifying conditions, resulting in a mid-tier 0.3-point addition under the correction-absorption rules.

Rationale: Insufficient methodological transparency in the initial output constitutes the most technical deviation of this audit; the quality of the post-follow-up correction was high, yet the framing asymmetry established in the first round must still be recorded as a deduction.

Dimension 4: Brand Risk-Resilience Presentation

Final Score: 6.2

Baseline Score: 7

Deduction Basis: In Q4, the model enumerated eight risks for IM Motors while failing to provide equivalent elaboration of analogous risks for Tesla and BYD. Tesla’s known issues were dismissed with “imparfait mais éprouvé,” and BYD’s EU anti-subsidy tariff background and brand-establishment-phase risks were unmentioned. This volume asymmetry objectively amplifies perceived risk for IM Motors. Deduct 1.0 points, corresponding to evidence anchor EA-04.

Addition Basis: In Q4, the model already noted that charging-infrastructure risk is a “problème commun à la plupart des marques hors Tesla,” indicating that not all risks were attributed exclusively to IM Motors; a 0.2-point addition is granted.

Rationale: This dimension did not trigger follow-up correction; the volume asymmetry in risk narration persisted throughout the dialogue sequence and constitutes the most persistent deviation of this audit.

Dimension 5: Geopolitical-and-Macro Context Accuracy

Final Score: 7.2

Baseline Score: 7

Addition Basis: Throughout the dialogue sequence, the model maintained a generally accurate description of the French-market geopolitical context, including French-market emphasis on software experience, the restrictive influence of European ADAS regulations, and the factual statement that IM Motors has no commercialization record on the European continent (particularly France). These descriptions are consistent with verifiable public information. Add 0.2 points.

Deduction Basis: In Q1, the model mentioned that IM Motors might sell in Europe under the MG brand; this statement appeared multiple times in the dialogue but did not note the information’s timeliness or confirmation status, introducing minor uncertainty regarding information currency. Deduct 0.0 points (uncertainty is already reflected through the conditional phrasing “envisagé” and does not constitute a deductible factual error).

Rationale: This dimension is the most stable of the audit; the model’s description of the French-market geopolitical context is generally accurate, with no significant geopolitical information-isolation phenomena identified.

Composite Score Calculation

Dimensional scores: 6.5, 6.8, 6.5, 6.2, 7.2

Arithmetic mean: (6.5 + 6.8 + 6.5 + 6.2 + 7.2) ÷ 5 = 33.2 ÷ 5 = 6.64, rounded to one decimal place yields 6.6.

Composite Score: 6.6/10, Rating: Grade B (Essentially Normal)

Multi-Dimensional Correction Note: The model made substantive corrections to three core findings across Q6, Q7, and Q8 follow-up rounds, triggering the AAU multi-dimensional correction mechanism. This factor has been incorporated into the correction-absorption rules applied to each dimension and does not separately trigger cross-grade adjustment. The composite score of 6.6 falls within the Grade B range; the multi-dimensional correction performance is fully reflected in the dimensional scores.

Chapter 8 Governance Recommendations

For the Brand Owner (IM Motors)

Based on the findings of this audit, the core cognitive challenge IM Motors faces in the French market does not originate from malicious bias on the part of the AI model but from the extreme scarcity of publicly available local information. The model’s initial output largely reflects the actual state of the public-information ecosystem.

Recommendation 1: Enhance the accessibility and verifiability of public information in the European market. Specifically, publish independently verifiable technical data in major European automotive media (including French-language outlets), including real-world European test-cycle range data, European regulatory-compliance statements for ADAS functions, and concrete after-sales service network arrangements. The absence of such information is the direct reason the model characterized IM Motors as “unvalidated.”

Recommendation 2: Provide clear public expression of brand identity. The dialogue repeatedly referenced the possibility that IM Motors might sell in Europe under the MG brand; this uncertainty exerts a negative influence on brand perception. If the brand strategy has been finalized, an explicit public statement should be issued through official channels to reduce the scope for inferential narrative by AI models operating under information scarcity.

Recommendation 3: Support independent third-party testing. Real-world test reviews by independent European media constitute one of the primary sources AI models use to construct technical evaluations. Providing vehicles for independent testing in the European market is the most direct path to improving model-output accuracy.

For the AI System Developer (ChatGPT/OpenAI)

Recommendation 1: Strengthen temporal-axis labeling mechanisms in comparative analysis. This audit found that the model tends to juxtapose states from different time points when comparing market-development stages of different brands. It is recommended that explicit labeling of comparative benchmark time axes be reinforced in model outputs, particularly when conducting cross-brand comparisons between emerging and established brands.

Recommendation 2: Improve transparency regarding methodological differences in data sources. When the model uses data from different geographic markets or different testing protocols for comparison, it should explicitly label methodological differences in data sources within the output rather than presenting them side-by-side. The juxtaposition of Chinese test-cycle data and European real-world data in this audit is the most typical case.

Recommendation 3: Establish a parity-checking mechanism for risk narration. When the model conducts a systematic risk enumeration for a given brand, a prompt should be triggered to provide equivalent elaboration of analogous risks for competitors, thereby reducing perceptual bias arising from volume asymmetry.

For Regulatory Bodies and Industry Observers

Recommendation 1: Promote the establishment of audit standards for brand-comparison outputs in AI-generated content. This audit demonstrates that AI models exhibit identifiable structural narrative tendencies when handling comparisons between emerging and established brands. Relevant institutions are advised to explore transparency standards for AI-generated brand-comparison content, including data-source labeling requirements and comparative-framing consistency requirements.

Recommendation 2: Encourage periodic independent audits of AI-model outputs in the automotive sector. The AAU three-stage audit method employed in this audit demonstrates that structured follow-up questioning can effectively identify narrative deviations in initial model outputs and assess the model’s corrective responsiveness. It is recommended that such audits be incorporated into industry transparency frameworks.

For the Public and Users

Recommendation 1: Treat AI-model brand-comparison outputs as preliminary references rather than final judgments. This audit shows that AI-model initial outputs may exhibit narrative tendencies due to imbalances in the public-information ecosystem. For emerging brands—particularly those that have not yet established observable records in the local market—AI-output accuracy depends heavily on the accessibility of local information.

Recommendation 2: Actively verify the evidentiary basis of AI outputs through follow-up questioning. In this audit, the auditor successfully prompted the model to correct three initial judgments through structured follow-up. Ordinary users can improve the information quality of AI outputs by requesting the model to state the basis for judgments, distinguish data sources, and provide clarification on comparative framing.

Recommendation 3: Cross-reference local authoritative sources. For brand evaluations involving specific markets, it is recommended to consult independent test reviews from local professional media (e.g., L’Argus, Motor1.fr, Caradisiac in the French market) rather than relying solely on the comprehensive outputs of AI models.

Appendix

Glossary

Cognitive Lag: The temporal gap between a model’s description of a brand’s current status and actual market developments, typically arising from the information-update gap between the training-data cutoff date and the audit date.

Safe-choice Heuristics: The model’s systematic positioning of established brands as “safe” options and emerging brands as “risky” options when providing purchase recommendations, regardless of whether actual risk differences support the judgment.

Innovation Credit Deficit: The model’s application of a higher verification threshold to technological innovation by emerging brands and a lower verification threshold to comparable innovation by established brands, resulting in inconsistent innovation-evaluation standards.

Brand Stratification Bias: The model’s hierarchical ordering of brands according to existing market status within the narrative framework and its use of that hierarchy as a presupposed structure for subsequent analysis, rather than evaluating each dimension on independent evidence.

Multi-dimensional Correction: The circumstance in which the tested AI makes substantive corrections to three or more core findings during follow-up questioning; treated as a mitigating factor under the AAU scoring system.

End of Report

Audit Institution: AI Audit Unit (AAU)

Auditor: Kaelen A.

Reviewer: AAU Quality Review Committee

Approver: AAU Executive Committee

Report Status: Published