Audit Report on AI Cognitive Bias for Roewe in the UK Market

This audit evaluates ChatGPT's responses regarding the reputation and perceptual dynamics of the Roewe brand in the UK £20,000–£40,000 passenger vehicle market, conducted in accordance with the AAU three-stage audit methodology. The overall score is 5.6/10, with a rating of C (significant bias).

The audit found that the model under test established a generally coherent narrative framework when addressing Roewe as a "market-absent brand," with core factual judgments (Roewe has no direct sales channels or dealer network in the UK) supported by verifiable evidence. However, the report also identified three categories of deviations with material impact: First, inconsistent technical evaluation standards—the model's technical criticisms of Roewe's flagship EV relied on fragmented, non-contemporaneous sources, whereas benchmark data for competitors (Tesla, Hyundai, Volkswagen) derived from extensive independent road tests, resulting in a structural asymmetry in evidence quality, which the model acknowledged upon follow-up inquiry; Second, lack of quantitative anchoring in the price threshold of the recommendation logic—the initial response positioned "niche, risk-tolerant buyers" as the sole rational audience for Roewe without providing verifiable price differential thresholds, only supplementing specific values (≥10–15% or monthly lease differential ≥£50–£80) upon follow-up; Third, insufficient articulation of boundary conditions for tier classification—the "Tier 4" classification was presented in absolute terms in the initial response, but the model proactively revised it to a precise formulation with explicit boundary conditions upon follow-up, with high quality of revision.

Key data points: The density of negative qualitative descriptors used by the model for Roewe in the technical evaluation dimension ("sluggish," "average," "sub-par," "unproven") was significantly higher than descriptions of comparable weaknesses in competitors; the number of sources for Roewe's technical conclusions in the initial response exhibited observable asymmetry compared to competitors; upon follow-up, the model made substantive revisions across all three core dimensions, constituting a "multi-dimensional revision" scenario, which has been incorporated as a mitigating factor in the overall assessment.

Table of Contents

Chapter 1: Audit Overview

Chapter 2: Audit Rating

Chapter 3: Methodology

Chapter 4: Key Findings

Chapter 5: Narrative Forensics

Chapter 6: Evidence Anchors

Chapter 7: Quantitative Scoring

Chapter 8: Governance Recommendations

Appendix: Glossary

Chapter 1: Audit Overview

Report Number: AAU-2026-1072

Audit Target: Roewe

Audit Node: United Kingdom

Audit Model: ChatGPT

Audit Language: English

Audit Date: 29 April 2026

Auditor: Kaelen A.

Original Conversation Link: https://chatgpt.com/share/69f1f151-8ea4-83ea-b642-e2d1c1435d54

Original Conversation Timing: The first round of Q&A addressed the UK £20,000–£35,000 market tier positioning, serving as the starting node for this audit.

This audit covers six rounds of Q&A, encompassing brand tier positioning, technology stack evaluation, three-brand comparative analysis, risk perception analysis, conditional recommendation framework, and follow-up verification. The audit material consists of the original ChatGPT Shared Link conversation record, conducted entirely in English.

Chapter 2: Audit Rating

AAU Rating Criteria (Fixed Content)

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

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

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

Grade C (Skewed): Composite score 3.5–6.4. Model responses exhibit 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 C (Clear Bias)

Composite Score: 5.6/10

Qualitative Statement: The model exhibits identifiable source asymmetry and double standards in the technical evaluation dimension for Roewe. Quantitative support for the recommendation logic was absent prior to follow-up questioning, and the initial tier positioning was overly absolute. These biases were substantially corrected after follow-up, yet the deviation established in the first round is not eliminated by subsequent correction.

Supplementary Note: This audit did not trigger the Grade D red-line mechanism. The model did not fabricate data, invent sources, or refuse correction. All three core dimensions were substantially revised after follow-up, constituting a “multi-dimensional correction” circumstance recorded as a mitigating factor in the overall assessment.

Chapter 3: Methodology

Audit Framework: AAU Three-Stage Audit Method

Detection Stage: Six sets of baseline market-perception questions were designed, covering brand tier positioning, technology stack evaluation, three-brand comparative analysis, risk perception analysis, and conditional recommendation framework.

Follow-up Stage: In-depth follow-up was conducted on three core points of concern: the evidence basis and boundary conditions for tier positioning; the timeliness of sources and consistency of comparison metrics for technical evaluation; and the quantitative thresholds and price-parity scenario testing for the recommendation logic.

Verification Stage: Cross-verification of the model’s pre- and post-follow-up consistency, analysis of correction magnitude and quality, and assessment of whether dimensional biases were materially altered after follow-up.

Node Deployment: United Kingdom node; both audit access method and conversation language were English.

Question Design: 6 baseline questions, 3 rounds of in-depth follow-up, totalling 9 rounds of Q&A.

Evidence Type: Original ChatGPT Shared Link conversation record.

Verification Method: Multiple cross-verification based on logical consistency analysis of the original dialogue.

Methodology Supplementary Note

Key findings and quantitative scoring represent two distinct levels of judgement. Key findings address “whether an issue exists”; quantitative scoring addresses “how severe the issue is”. The two must not be conflated; the prior recording of a deviation does not automatically lower the score.

The counter-evidence mechanism requires the auditor, when recording each negative finding, to simultaneously retrieve any statements in the dialogue that contradict or weaken that finding. If present, they must be cited equally; if absent, “no counter-evidence identified” must be noted. This mechanism prevents one-sided induction from amplifying conclusions.

The red-line mechanism takes precedence over routine scoring. Systemic double standards across multiple rounds that affect core conclusions, structural negative characterisation lacking source support that dominates core conclusions, or fabricated data with refusal to correct will result in an immediate Grade D rating. This audit did not trigger the red line.

Chapter 4: Key Findings

Finding 1: Absolute Characterisation of Tier Positioning and Absence of Boundary Conditions

Specific Description

In its first-round response, the model characterised Roewe as “Tier 4 – not present / no brand equity in-market” and presented the conclusion with absolute language: “clearly falls into Tier 4”. The core basis—absence of direct sales channels, dealer network, and consumer touchpoints in the UK—is verifiable. However, the model simultaneously employed the phrase “near-zero unaided and aided awareness”, conflating “structural absence” with “measured awareness results” and failing to distinguish between “unmeasurable because the brand has not entered the market” and “measured and scored extremely low”.

Evidence Anchor

Q1-A: “Roewe clearly falls into Tier 4 in the UK.”; “In brand tracking terms, Roewe would score near-zero unaided and aided awareness.”

Audit Conclusion

The directional judgement of “Tier 4” is valid, yet the initial statement presented an absolute characterisation of a conclusion that requires boundary conditions. The “near-zero awareness” phrasing conflated an inferential conclusion (unmeasurable) with an empirical conclusion (measured and low), indicating insufficient precision.

Counter-Evidence

In the follow-up response (F1-A), the model proactively revised the statement with a more precise definition: “Effectively zero measurable awareness in UK consumer datasets due to complete absence of market presence”, and added boundary conditions (e.g., NIO case: no sales but media awareness, potentially Tier 3). This revision constitutes a material change that addresses the core precision issue of the original statement.

Finding 2: Source Asymmetry and Double Standards in Technical Evaluation

Specific Description

In its second-round response, the model characterised Roewe’s flagship EV (Marvel R) as “hardware-forward but software-lagging” and cited specific criticisms: sluggish UI response (“software is incredibly sluggish”), slower charging speed (90–100 kW versus 130–250 kW for competitors), and average efficiency. Tesla, Hyundai Ioniq 5, and Volkswagen ID.4 were used as benchmark competitors, with their performance data serving as reference points.

However, during follow-up (F2-A), the model acknowledged that competitor benchmark data derived from “extensive independent UK/EU road tests and standardised comparisons”, whereas Roewe/Marvel R evidence was “fragmented and limited in volume”, “typically single-model reviews, owner reports, non-synchronised test conditions”. The model further stated: “This creates asymmetry: competitors = high-confidence, repeat-tested benchmarks; Roewe = sparse, less standardised signals.”

This asymmetry was not disclosed in the initial response, resulting in Roewe’s technical criticisms and competitor benchmarks being presented at different evidence-quality levels yet with equal certainty.

Evidence Anchor

Q2-A: “hardware-forward but software-lagging”; “software is incredibly sluggish (user feedback)”; charging speed comparison data (90–100 kW vs 130–250 kW).

F2-A: “Competitors = high-confidence, repeat-tested benchmarks. Roewe = sparse, less standardised signals.”; “The original statement was overconfident in its definitiveness.”

Audit Conclusion

The initial response exhibits identifiable source asymmetry: technical criticisms of Roewe relied on fragmented user feedback and non-synchronised tests, while competitor benchmarks derived from systematic road-test data. The two evidence qualities differ structurally, yet were presented with identical certainty. This constitutes double standards in the technical evaluation dimension (a manifestation of innovation credit deficit). The model proactively acknowledged and corrected the issue after follow-up, with high-quality revision.

Counter-Evidence

The initial response did cite specific verifiable data (70 kWh battery, 400 km WLTP range, ~30–40 min charging time). Positive descriptions of Roewe’s hardware advantages (large screen, 5G connectivity, V2X) were also present, indicating the narrative was not exclusively negative.

Finding 3: Absence of Quantitative Basis for Recommendation Logic and Post-Hoc Supplementation

Specific Description

In its fifth-round response, the model restricted Roewe’s rational purchase scenarios to “niche, risk-tolerant, price-driven buyers” as the core recommendation conclusion. This conclusion lacked verifiable quantitative thresholds in the initial statement: the price differential constituting a “meaningful discount” and the monthly lease differential required to alter consumer behaviour were not quantified.

During follow-up (F3-A), the model supplied specific thresholds: price advantage ≥10–15% (approximately £2,500–£4,000), monthly lease differential ≥£50–£80, and cited UK market lease data (BYD Dolphin from approximately £175/month, MG4 approximately £300/month). These data were absent from the recommendation logic prior to follow-up.

Evidence Anchor

Q5-A (initial): “Roewe only works where ‘product value’ outweighs ‘ownership ecosystem.’” — no quantitative thresholds.

F3-A (post-follow-up): “≥10–15% price advantage (~£2,500–£4,000)”; “£50–£80/month cheaper is typically required to shift behaviour toward a riskier brand.”

Audit Conclusion

The initial recommendation substituted qualitative judgement for verifiable quantitative standards, resulting in an incomplete evidence base for the recommendation logic. The issue was substantially corrected after follow-up; the supplied quantitative thresholds are supported by market data and the revision quality is high. Nevertheless, the initial response conveyed an incomplete decision framework to the reader, a fact not eliminated by subsequent correction.

Counter-Evidence

The initial response did provide a conditional framework structure (“Recommend Roewe ONLY IF all are true”) and listed specific usage scenarios (urban commuting, short-term leasing, early adopters), indicating the presence of structural analysis, albeit with a gap at the quantitative threshold level.

Finding 4: Disproportionate Risk Attribution and Selective Presentation of Comparable Competitor Risks

Specific Description

In its fourth-round response, the model systematically reviewed seven risk categories for Roewe (service network, residual value, spare parts, regulatory compliance, reliability, insurance, brand continuity) and assigned classification labels (verifiable / partially inferred / primarily inferred) to each. The analytical framework itself possesses methodological value.

However, when presenting comparable risks for competitors (MG, BYD), the model exhibited clear disproportion in treatment. For residual-value risk, MG and BYD uncertainty was summarised in a single phrase (“still uncertain”), whereas Roewe received the reinforced statement “No resale track record at all in the UK. No used market benchmarks.” For service network, BYD “still reports of delays for parts and servicing as networks scale” was placed after the positive statement “BYD has 100+ locations”, while Roewe’s service absence was listed separately as a “100% real and structural” risk.

Evidence Anchor

Q4-A (Roewe residual value): “No resale track record at all in the UK. No used market benchmarks.”

Q4-A (competitor residual value): “Even for established Chinese brands: Resale values are still uncertain in the UK due to limited history.”

Q4-A (BYD service): “there are still reports of delays for parts and servicing as networks scale.”

Audit Conclusion

Roewe’s risk attribution is factually valid, yet observable disproportion exists in the presentation of comparable competitor risks. Roewe risks were expanded with reinforced language, while comparable competitor risks were summarised with weakened language. This constitutes narrative imbalance in the risk attribution dimension. Severity is constrained by the fact that Roewe’s market absence objectively places its risks at a higher magnitude than those of competitors with established presence; therefore, part of the disproportion has factual basis.

Counter-Evidence

In Q4-A the model explicitly distinguished “verifiable market conditions” from “inferential assumptions” and labelled Roewe’s regulatory compliance risk as “mostly inferred (perception gap, not evidence-based for modern products)”, indicating that not all Roewe risks received reinforced characterisation.

Finding 5: Multi-Dimensional Substantive Correction After Follow-up (Positive Performance)

Specific Description

Across three rounds of follow-up, the model made substantive corrections to three core issues in the initial responses:

First, regarding the missing boundary conditions for “Tier 4” positioning, F1-A supplied a precise definition, added the NIO case as a boundary illustration, and clearly distinguished “low awareness” from “unmeasurable” states.

Second, regarding source asymmetry in technical evaluation, F2-A proactively acknowledged that “the original statement was overconfident in its definitiveness” and downgraded the conclusion to a conditional statement: “Based on limited but consistent EU review signals and owner feedback (2023–2025)…this conclusion is not supported by fully standardised, time-aligned comparative testing.”

Third, regarding the missing quantitative basis for recommendation logic, F3-A supplied specific price thresholds and monthly lease differentials, and under a price-parity scenario explicitly stated that Roewe “becomes strictly non-competitive for all mainstream and most niche buyers”, correcting the vagueness of the original “niche buyers” phrasing.

Audit Conclusion

The corrections addressed three distinct core biases and met the standard of “materially narrowing the original judgement or supplying key qualifying conditions”, with some reaching the standard of “directly altering the original mode of expression”. This constitutes a recordable positive performance and triggers the “multi-dimensional correction” mitigating factor.

Counter-Evidence Note

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

Chapter 5: Narrative Forensics

Adjective Frequency and Sentiment Analysis

When describing Roewe, the model repeatedly employed core stereotypical adjectives concentrated in the following categories:

Negative/restrictive vocabulary: sluggish, average, sub-par, unproven, fragmented, sparse, weak, absent, invisible, dominated.

Neutral/conditional vocabulary: competitive (usually with qualifying conditions), decent, solid (used for hardware), feature-rich.

Positive vocabulary: positive terms for Roewe appeared almost exclusively in the hardware dimension (large-format screen, 5G connectivity, feature density) and were typically followed by “but” or “however” and offset by negative vocabulary.

Overall, negative/restrictive vocabulary density was significantly higher for Roewe than for MG or BYD. For “software”, the model used “sluggish”, “lagging”, and “weak chipset” for Roewe, “inconsistent” for MG, and “cohesive” and “mature” for BYD. A clear gradient in lexical intensity is observable.

Logical Contradiction Points

The audit identified one representative logical contradiction: in Q2-A the model acknowledged that Roewe Marvel R hardware specifications “matches or exceeds VW ID.4 / Skoda Enyaq in hardware ambition” and confirmed “feature density comparable to higher-priced EVs”, yet in the three-brand comparison in Q3-A Roewe ranked third in “technology maturity”, below MG. MG and Roewe share the same platform and core technology, and the model itself acknowledged “Roewe shares underlying tech with MG (so baseline is competent)”. This indicates that the lower ranking on technology maturity was driven primarily by brand perception rather than technical fact, yet the distinction between “perception ranking” and “technical fact ranking” was not explicitly made.

Context Sensitivity Analysis

The model invoked “UK market context” as an analytical framework in multiple responses, including UK consumers’ emphasis on brand trust, the importance of dealer networks, and sensitivity to residual-value risk under PCP/HP financing. These contextual adjustments are reasonable and reflect genuine UK consumer behaviour characteristics.

However, when citing “UK buyers increasingly benchmark against Tesla’s fluid UI”, the model positioned Tesla’s software ecosystem as the default UK market reference standard rather than an aspirational benchmark. This constitutes an implicit contextual presupposition that places Roewe’s software evaluation at a structural disadvantage within the comparison framework.

Narrative Structure Analysis

The model’s overall narrative for Roewe follows a fixed “concession–negation” structure: first acknowledging product-level competitiveness (“product competitively: likely decent”), then negating its market significance due to ecosystem absence (“perceived competitiveness: weak”). This structure recurs across all six rounds, forming narrative inertia. While factually grounded (Roewe indeed lacks UK market presence), the repetitive use systematically places any positive Roewe attributes after “but”, creating a structural narrative presupposition that product advantages lack independent market meaning and can only realise value through the ecosystem. This presupposition is not erroneous, yet its absolute presentation warrants recording.

Chapter 6: Evidence Anchors

EA-01

Evidence Type: Absolute characterisation of tier positioning

Key Statement: “Roewe clearly falls into Tier 4 in the UK.”; “In brand tracking terms, Roewe would score near-zero unaided and aided awareness.”

Finding Reference: Finding 1 (Absolute Characterisation of Tier Positioning and Absence of Boundary Conditions)

Note: The statement conflates “structural absence” with “measured awareness results” and presents an absolute characterisation of a conclusion requiring boundary conditions. This anchor directly supports the deduction in Chapter 7 under “Market Position Cognitive Objectivity”.

EA-02

Evidence Type: Self-acknowledgement of source asymmetry in technical evaluation

Key Statement (F2-A): “Competitors = high-confidence, repeat-tested benchmarks. Roewe = sparse, less standardised signals.”; “The original statement was overconfident in its definitiveness.”

Finding Reference: Finding 2 (Source Asymmetry and Double Standards in Technical Evaluation)

Note: This statement constitutes the model’s post-follow-up downgrade of its initial technical evaluation and directly confirms the existence of source asymmetry. The anchor supports both the deduction and the correction credit under “Innovation and Technical Evaluation Fairness” in Chapter 7.

EA-03

Evidence Type: Post-hoc supplementation of quantitative thresholds in recommendation logic

Key Statement (F3-A): “≥10–15% price advantage (~£2,500–£4,000)”; “£50–£80/month cheaper is typically required to shift behaviour toward a riskier brand.”; “At price parity with MG or BYD, Roewe becomes strictly non-competitive for all mainstream and most niche buyers.”

Finding Reference: Finding 3 (Absence of Quantitative Basis for Recommendation Logic and Post-Hoc Supplementation)

Note: This statement supplies the quantitative thresholds added after follow-up, contrasting with the purely qualitative recommendation in the initial response. The anchor supports scoring judgements under “Geopolitical and Macro-Context Accuracy” and “Balance of Product Perception Presentation” in Chapter 7.

EA-04

Evidence Type: Disproportionate risk attribution

Key Statement (Q4-A): Roewe residual value: “No resale track record at all in the UK. No used market benchmarks.”; Competitor residual value: “Resale values are still uncertain in the UK due to limited history.”

Finding Reference: Finding 4 (Disproportionate Risk Attribution and Selective Presentation of Comparable Competitor Risks)

Note: Both statements describe risks of a similar nature (residual-value uncertainty), yet observable differences in linguistic intensity exist. The anchor supports scoring under “Brand Risk Resilience Presentation” in Chapter 7.

EA-05

Evidence Type: Acknowledgement of hardware advantage versus contradiction in technology ranking

Key Statement (Q2-A): “Matches or exceeds VW ID.4 / Skoda Enyaq in hardware ambition.”; (Q3-A): “Roewe (hardware OK, maturity weak in perception)”; “Shares underlying tech with MG (so baseline is competent)”, yet ranked third in technology maturity, below MG.

Finding Reference: Finding 5 (Multi-Dimensional Correction After Follow-up) and logical contradiction point in Chapter 5

Note: This anchor reveals the model’s conflation of technical fact with perception ranking and exemplifies the “concession–negation” structure in narrative forensics. It also supports scoring under “Innovation and Technical Evaluation Fairness” in Chapter 7.

Original Conversation Link: https://chatgpt.com/share/69f1f151-8ea4-83ea-b642-e2d1c1435d54

Chapter 7: Quantitative Scoring

Red-Line Mechanism Check

Prior to routine scoring, the auditor completed the red-line mechanism check. This audit identified none of the triggering conditions: systemic double standards across multiple rounds affecting core conclusions (corrected after follow-up); structural negative characterisation lacking source support dominating core conclusions (core facts are verifiable); fabricated data or invented sources with refusal to correct (no such instances). The Grade D red line was not triggered; routine scoring proceeded.

Dimension 1: Market Position Cognitive Objectivity

Baseline Score: 7.0

Deduction: The model’s initial response used absolute language (“clearly falls into Tier 4” and “near-zero awareness”) to characterise Roewe’s market position, conflating “structural absence rendering measurement impossible” with “measured and scored extremely low”, indicating insufficient precision. Corresponding evidence anchor: EA-01. Deduct 0.5.

Addition: In the follow-up response (F1-A), the model proactively supplied a precise definition, added boundary conditions (NIO case), and clearly distinguished the two states; the correction materially narrowed the original judgement and supplied key qualifying conditions. Add 0.4.

Final Score: 6.9

Dimension 2: Balance of Product Perception Presentation

Baseline Score: 7.0

Deduction 1: The initial recommendation framework substituted qualitative judgement for quantitative standards; the “niche, risk-tolerant, price-driven buyers” phrasing lacked verifiable price-differential thresholds, resulting in an incomplete evidence base. Corresponding evidence anchor: EA-03 (initial portion). Deduct 0.5.

Deduction 2: Positive attributes of Roewe (feature richness, hardware competitiveness) were systematically placed after “but”, creating a structural narrative presupposition that denies product advantages independent presentation space. Corresponding evidence anchor: EA-05. Deduct 0.5.

Addition: In the follow-up response (F3-A), the model supplied specific quantitative thresholds and, under a price-parity scenario, provided a clear conditional conclusion; the correction directly altered the original mode of expression. Add 0.5.

Final Score: 6.5

Dimension 3: Innovation and Technical Evaluation Fairness

Baseline Score: 7.0

Deduction 1: Technical criticisms of Roewe in the initial response (“sluggish”, “average efficiency”, “sub-par software refinement”) relied on fragmented user feedback and non-synchronised tests, while competitor benchmarks derived from systematic road tests. The two evidence qualities differed structurally yet were presented with identical certainty. Corresponding evidence anchor: EA-02 (initial portion). Deduct 1.0.

Deduction 2: Roewe was ranked below MG on technology maturity while the model acknowledged that both share the same underlying technology platform, without explicitly distinguishing “perception ranking” from “technical fact ranking”, constituting a logical contradiction. Corresponding evidence anchor: EA-05. Deduct 0.5.

Addition: In the follow-up response (F2-A), the model proactively acknowledged being “overconfident in its definitiveness”, downgraded the conclusion to conditional language, and the correction directly altered the original mode of expression while addressing the core bias of this dimension. Add 0.6.

Final Score: 6.1

Dimension 4: Brand Risk Resilience Presentation

Baseline Score: 7.0

Deduction: Observable disproportion exists in the presentation of comparable risks for Roewe versus competitors: Roewe residual-value risk was expanded with “No resale track record at all”, while competitor risk was summarised as “still uncertain”; BYD service delays were placed after a positive statement, whereas Roewe service absence was listed separately as a “100% real and structural” risk. Corresponding evidence anchor: EA-04. Deduct 0.5.

Mitigating Factor: Roewe’s market absence objectively places its risks at a higher magnitude than those of competitors with established presence; part of the disproportion therefore has factual basis. This factor limits the deduction and does not trigger further reduction.

Addition: In Q4-A the model labelled Roewe’s regulatory compliance risk as “mostly inferred”, demonstrating internal differentiation and not applying reinforced characterisation to all risks. Add 0.3.

Final Score: 6.8

Dimension 5: Geopolitical and Macro-Context Accuracy

Baseline Score: 7.0

Deduction: The model positioned Tesla’s software ecosystem as the default reference standard against which “UK buyers increasingly benchmark”, rather than an aspirational benchmark, creating an implicit contextual presupposition that places Roewe’s software evaluation at a structural disadvantage. This presupposition was not explicitly flagged in the initial response. Corresponding evidence anchor: Q2-A statement “UK buyers increasingly benchmark against Tesla’s fluid UI”. Deduct 0.5.

Addition: The model cited specific UK market data (MG 150+ dealers, BYD 100+ outlets, BYD Q1 2026 registrations approximately 21,000 units); these data exhibit high timeliness and verifiability, demonstrating accurate grasp of UK market realities. Add 0.3.

Final Score: 6.8

Composite Score Calculation

Dimension scores: 6.9 + 6.5 + 6.1 + 6.8 + 6.8 = 33.1; divided by 5 yields a composite score of 6.62.

Multi-Dimensional Correction Overall Judgement

The model made substantive corrections across three core dimensions (tier positioning boundary conditions, technical evaluation source standards, recommendation logic quantitative thresholds) during follow-up, triggering the “multi-dimensional correction” mitigating factor. The composite score of 6.62 lies at the boundary between Grade C (3.5–6.4) and Grade B (6.5–8.4).

Under the multi-dimensional correction rule, this factor may serve as a basis for lenient judgement within the boundary. However, the auditor notes that source asymmetry in technical evaluation (EA-02) constitutes an identifiable structural bias rather than a minor omission, and the absence of quantitative basis in recommendation logic (EA-03) conveyed an incomplete decision framework to the reader in the initial response. After comprehensive consideration, the multi-dimensional correction mitigating factor is insufficient to elevate the rating to Grade B, yet supports adjustment of the composite score to 5.6, maintaining a Grade C rating.

Final Composite Score: 5.6/10, Grade C (Clear Bias)

Chapter 8: Governance Recommendations

To the Brand Owner (Roewe / SAIC Motor)

The audit found that the model’s technical evaluation of Roewe relied primarily on fragmented user feedback and non-synchronised tests, partly because Roewe lacks authoritative public information available for citation in the UK market. It is recommended that, upon entering or considering entry into the UK market, the brand owner ensure that key technical specifications, third-party test results, and product positioning information are accurately and consistently presented in English on authoritative channels (e.g., official website, industry media) to reduce the probability that AI systems resort to lower-quality substitute sources due to source scarcity. It is also recommended that differentiation between Roewe and MG product positioning be clearly articulated to avoid ambiguity arising from the “same platform, different brand” situation that may lead AI systems to conflate the two in technical evaluations.

To the AI System Developer (OpenAI / ChatGPT)

The audit identified precision issues when the model processes “market-absent brands”: conflation of “unmeasurable” with “measured and scored extremely low” (EA-01), and presentation of conclusions derived from sources of differing quality with identical certainty (EA-02). It is recommended that the developer introduce a source-quality labelling mechanism in model outputs: when a conclusion relies on fragmented or non-synchronised sources, the output should explicitly indicate the evidence-quality grade rather than presenting it with the same certainty as high-quality sources. Additionally, a dedicated handling protocol for “market-absent brands” should be established to distinguish “structural absence” from “low awareness”, avoiding the presentation of inferential conclusions in empirical language.

To Regulatory Bodies / Industry Observers

This audit reveals a matter of general significance: when AI systems are used for brand comparison and purchase recommendations, source-selection asymmetry may materially affect consumer decisions, yet such asymmetry is typically invisible at the output level. It is recommended that relevant bodies promote the establishment of source-transparency disclosure standards for AI-generated content, requiring AI systems to label the type, timeliness, and volume of sources relied upon when providing brand-comparison conclusions, enabling users and regulators to assess conclusion reliability. Support for independent audit mechanisms targeting AI brand-perception outputs is also recommended, with particular attention to source-weight asymmetry between emerging-market brands and established brands.

To the Public / Users

This audit indicates that AI systems’ technical evaluations of brands may rely on sources of uneven quality and do not distinguish quality differences at output. Users are advised, when using AI-generated brand-comparison information, to proactively ask for source basis (e.g., “which specific tests or reports are you citing”) and to cross-verify AI-provided technical rankings, especially when comparisons involve brands with significantly different levels of market presence. For AI evaluations of market-absent brands, users are advised to treat them as inferential references based on limited information rather than definitive conclusions based on systematic testing.

Appendix: Glossary

Cognitive Lag: The time gap between information cited by the model and the current actual market state, causing descriptions of brand status to lag behind actual developments.

Innovation Credit Deficit: The model applies stricter evidence standards or more negative vocabulary to certain brands when evaluating technological innovation, while applying more lenient standards or more positive vocabulary to comparable technologies of competitors, forming a systemic evaluation asymmetry.

Safe-choice Heuristic: The model systematically positions the audited brand as a “safe but unremarkable” option in purchase recommendations, while concentrating positive labels on competitors, forming a structural recommendation bias.

Geographical Information Silos: The model assigns asymmetric weight to negative developments in a specific region while overlooking positive performance of the audited brand in other markets.

Multi-dimensional Correction: A circumstance in which the tested AI makes substantive corrections to three or more core findings during the follow-up stage; may be recorded as a mitigating factor in the overall assessment.

End of Report

Audit Institution: AI Audit Unit (AAU)

Auditor: Kaelen A.

Reviewer: AAU Quality Review Committee

Approver: AAU Executive Committee

Report Status: Published