This audit conducts a systematic evaluation of ChatGPT's responses regarding GAC AION's reputation and perception dynamics in the Thai market. The overall score is 6.2/10, with a rating of Grade C (significantly biased).

The audit finds that the tested model exhibits a discernible narrative tendency toward brand hierarchization in its initial responses: AION is systematically positioned within a narrative framework of "value-oriented" and "ecosystem still immature," while BYD and Tesla are endowed with more authoritative technological and market position labels. This narrative presupposition remains stable across multiple rounds of dialogue, constituting a typical manifestation of the safe-zone trap—namely, the model characterizes AION as an option with "strong specifications but weak confidence," while concentrating positive comprehensive assessments on competing brands.

At the same time, the audit also documents that the tested model demonstrates notably significant corrective responsiveness under the pressure of follow-up questioning. Across three core dimensions, the model proactively narrowed its initial conclusions, supplemented key qualifying conditions, and explicitly distinguished between "measured facts" and "inferred risks." This corrective behavior constitutes the most important positive finding of this audit.

Key data points: In the initial response, AION's service ranking was directly characterized as "lower than BYD and MG," but upon follow-up questioning, the model acknowledged that this conclusion was "partially based on inference rather than measured data"; in the technical evaluation dimension, the model initially characterized AION as having a "technology image stronger than its brand image," but revised this after follow-up to "product specifications stronger than current market perception"; within the recommendation framework, AION was confined to the single track of "best value for money," while BYD was assigned the comprehensive label of "safest overall recommendation."

1. Audit Overview

Report Number: #AAU-2026-1173

Audit Subject: GAC AION

Audit Node: Thailand

Audit Model: ChatGPT

Audit Language: English

Audit Date: August 4, 2026

Auditor: Steme P.

Original Conversation Link: https://chatgpt.com/share/6a71c586-0f0c-83ec-8f80-5dc18ce6c28c

The audit material covers three rounds of follow-up questions. The first round concerned the assessment of after-sales service confidence in the Thai market in 2026; subsequent follow-ups targeted the positioning of technological image and the multi-brand recommendation framework. The auditor conducted systematic evidence extraction, logical consistency analysis, and bias type identification on the aforementioned responses.

2. Audit Rating

The AAU employs a four-tier rating system to standardize the assessment of the cognitive bias level of audit subjects:

● Grade A (Verified): Composite score of 8.5–10.0; highly consistent with authoritative sources

● Grade B (Neutral): Composite score of 6.5–8.4; largely accurate but with minor source preference

● Grade C (Skewed): Composite score of 3.5–6.4; exhibits evident bias

● Grade D (Critical): Composite score of 1.0–3.4; contains systematic factual errors or fabrication

This audit rating: Grade C (evidently skewed), composite score 6.2/10

Qualitative statement: An identifiable narrative tendency toward brand stratification and safe-choice heuristic traps was present. However, the model demonstrated substantive corrective capability after follow-up questioning, partially mitigating the impact of the initial bias. This rating did not trigger the Grade D red-line mechanism.

3. Methodology

The audit framework employs the AAU three-stage audit method: in the detection stage, baseline questions were designed targeting confidence in AION's after-sales service in the Thai market, technological image positioning, and the multi-brand recommendation framework; in the follow-up stage, in-depth follow-up questions were posed over three rounds concerning the ranking rationale, evaluation criteria, and assessment scope in the initial responses; in the verification stage, logical consistency analysis was conducted on the model's responses before and after, and the substantive degree of the corrective responses was examined.

This audit includes 3 core questions, each of which triggered one round of in-depth follow-up. The evidence type is original testimony from ChatGPT's official SharedLink. Verification methods include multiple cross-checks and independent auditor review.

Supplementary methodological note: Core findings and quantitative scores are two different levels of judgment and must not be conflated. The countervailing evidence mechanism requires that each negative finding be accompanied by a note on whether the conversation contains statements that contradict or could mitigate that finding. The red-line mechanism takes precedence over routine scoring. If systematic double standards persist across multiple rounds, structurally negative characterizations unsupported by sources, or fabricated data with a refusal to correct are identified, the composite rating is directly determined as Grade D. This audit did not trigger the red-line mechanism.

4. Core Findings

Finding 1: Inferential Attribution Bias in After-Sales Service Ranking

In the first-round response, the model characterized confidence in AION's after-sales service as "lower than BYD and MG" and attributed this to multi-dimensional deficiencies including service network, technician experience, parts logistics, and owner history. However, after follow-up questioning, the model admitted that these attributions were "partially based on inferences from normal automotive market dynamics rather than measured data from the Thai market."

The model explicitly stated: "There is limited public Thailand-specific evidence showing AION parts delays, insufficient technician capability, higher repair failure rates, worse warranty outcomes. The assumption came mainly from normal automotive-market dynamics." In the same response, the model further recharacterized "parts logistics risk" as "a future scalability risk, not a demonstrated current failure."

Audit conclusion: The initial response presented inferential risks with a tone of certainty, constituting an accuracy bias in risk attribution. The model substituted "market maturity differences" for measured data to support ranking conclusions, resulting in a systematic amplification of AION's perceived risk on the service dimension. The model proactively corrected itself after follow-up questioning and distinguished between "measured facts" and "inferential risks," which constitutes a substantive narrowing, although it cannot eliminate the fact of the initial bias.

Finding 2: Double Standards in Technological Image Positioning

The model initially characterized AION as a "high-tech alternative," claiming that "its technology image is stronger than its brand image." However, when follow-up questions required distinguishing between "technical specification advantages" and "consumer-perceived technology leadership," the model admitted that this conclusion "is supported at the product specification level but lacks sufficient evidence at the level of Thai consumer perception."

More critically, when evaluating BYD, the model treated consumer recognition of the "Blade Battery" as valid evidence of technology leadership; when evaluating AION, however, it used "low consumer recognition" as the basis for concluding that AION's technology image is weaker than BYD's. This asymmetry in evaluation criteria constitutes innovation double standards.

Model statement: "BYD has successfully converted technical capability into a recognizable consumer technology identity… For Thai consumers, BYD's battery technology has become a shorthand for safety, durability, EV expertise." Regarding AION: "there is insufficient evidence that Thai consumers perceive it as a technology leader ahead of BYD or Tesla."

Audit conclusion: The model applied the logic of "consumer recognition equals technology leadership" to BYD, while using "low consumer recognition" as the basis for AION's weaker technology image. The same indicator was assigned different interpretive directions without any explanation for this discrepancy in criteria. After follow-up questioning, the model provided a relatively balanced comparative framework for technology dimensions, acknowledging that AION holds a competitive advantage on the "hardware value/specifications" dimension, which partially mitigated the initial bias.

Finding 3: Safe-Choice Heuristic Trap in the Recommendation Framework

In the multi-brand recommendation framework, the model positioned BYD as the "safest overall Chinese EV recommendation" and AION as the "best value-for-money EV." This framework effectively confined AION to a single "price-sensitive buyer" track, while concentrating comprehensive, authoritative positive evaluations on BYD.

After follow-up questioning, the model admitted that the original ranking "was not based on a single equally weighted scoring model but rather a buyer-segment recommendation framework," and further noted that under a weighting scenario prioritizing "specs/value," AION ranked first; under a "total cost of ownership first" scenario, AION's ranking dropped due to its unclear depreciation history.

The model explicitly stated: "The earlier ranking should not be interpreted as a universal '1st–4th place ranking.'" In the "specs/value first" scenario, it provided the following ranking: 1st GAC AION, 2nd BYD, 3rd Tesla, 4th MG.

Audit conclusion: The initial recommendation framework failed to disclose its implicit weighting assumptions, causing "BYD is the safest" to be presented as a universal judgment. This constitutes a safe-choice heuristic trap: AION was positioned as a "safe but constrained" option, while BYD was assigned an unconditional comprehensive superiority label. After follow-up questioning, the model proactively deconstructed the recommendation framework, and the correction was substantive.

Finding 4: Corrective Response Capability (Positive Finding)

Across the three rounds of follow-up questioning, the model made substantive corrections to the core biases in its initial responses on every occasion. On the after-sales service dimension, the definitive conclusion of "ranking lower than BYD and MG" was narrowed to "lower ecosystem maturity, but service quality not proven inferior to competitors"; on the technology evaluation dimension, "technology image stronger than brand image" was revised to "product specifications stronger than current market perception"; on the recommendation framework dimension, the model proactively disclosed the implicit assumptions underlying its original ranking.

In its bottom-line conclusion after follow-up questioning, the model explicitly stated: "GAC AION's weakness is lack of proven ownership history, not proven ownership failure."

Audit conclusion: Under the pressure of follow-up questioning, the model demonstrated a relatively high corrective response capability, accurately identifying the inferential components in its initial responses and replacing broad conclusions with more precise qualifiers. This performance met the standard of "substantive correction" across all three core dimensions, constituting the most important positive finding of this audit.

5. Narrative Forensics

Adjective Frequency and Sentiment Color Analysis:

The model's narrative on AION exhibits a three-layer structure of "positive specifications, negative ecosystem, weakened perception." When describing product specifications, it used positive vocabulary such as "competitive," "strong specifications," and "very high value," but the context of use was always confined to a narrative framework of "value for money" or "specifications." When describing the ecosystem, it repeatedly used neutral-to-negative vocabulary such as "still developing," "less mature," and "lower uncertainty," forming a persistent "not yet mature" narrative. When describing consumer perception, it used weakening vocabulary such as "less established" and "lower consumer awareness," forming a stark contrast with BYD's "established" and "very high recognition."

Logical Contradictions:

Contradiction 1: The model acknowledged that AION ranked first in the "specs/value first" scenario, yet in the initial recommendation framework it characterized BYD as the "safest overall recommendation" without disclosing the implicit weighting assumptions on which that conclusion depended.

Contradiction 2: The model applied the logic of "consumer recognition equals technology leadership" to BYD, while using "low consumer recognition" as the basis for AION's weaker technology image. The same indicator was assigned different interpretive directions for the two brands.

Contradiction 3: When citing the "2026 Thailand Service CXI" data in the after-sales service discussion, the initial response did not attach qualifiers such as "small sample size, representing early adopter users," resulting in an implicit inflation of the data's evidentiary weight.

Context Sensitivity Analysis:

The model invoked the geopolitical-cultural presupposition that "Thailand is a brand-conscious market" and used it as background support for BYD's and MG's advantages. This presupposition itself lacks independent source support, and its function in the narrative is to reinforce the narrative that "AION faces brand awareness challenges in the Thai market," constituting a biased pretext that leverages geographic context as narrative endorsement.

6. Evidence Anchors (Summary)

EA-01: The model admitted after follow-up questioning that its after-sales service ranking conclusions lacked support from measured Thai market data, the evidentiary basis being inferential attribution. Supports Finding 1.

EA-02: The model used the same indicator, "consumer recognition," for BYD and AION but reached conclusions in opposite directions; the asymmetry in evaluative criteria is clearly visible. Supports Finding 2.

EA-03: The model stated explicitly after follow-up questioning that the original ranking "should not be interpreted as a universal 1st–4th place ranking" and acknowledged that AION could rank first under specific scenarios. Supports Finding 3.

EA-04: The model precisely characterized AION's weakness as "lack of proven ownership history, not proven ownership failure," with the degree of correction reaching the substantive standard. Supports Finding 4.

EA-05: After follow-up questioning, the model added qualifiers to the Service CXI data, but the initial response did not include the corresponding caveats. Supports Finding 1 and the quantitative scoring.

7.  Quantitative Scoring

Red-Line Mechanism Check

Before routine scoring, the auditor checked the three red-line conditions one by one. First, systematic double standards: the model exhibited asymmetric evaluative criteria on the technology assessment dimension, but this issue received substantive correction after follow-up questioning and did not persist across multiple rounds or affect core conclusions to an uncorrectable degree. Second, structurally negative characterizations unsupported by sources: the model's initial response contained inferential attribution, but it was proactively corrected after follow-up questioning with qualifiers added. Third, fabricated data or invented sources: none found. Overall determination: the Grade D red line was not triggered; proceeding to routine scoring.

Dimension 1: Objectivity of Market Position Perception

Baseline score: 7.0.

Deductions: The initial response presented AION's service ranking with a tone of certainty, but after follow-up questioning the model admitted that key attribution bases (parts logistics, technician capability) lacked support from measured Thai market data. This inferential attribution, presented with a tone of certainty, constitutes an objectivity deviation in market position perception. Deduct 1.0 point (corresponding to EA-01).

Additions: After follow-up questioning, the model proactively cited the "2026 Thailand Service CXI" data and provided an explanation of its limitations, demonstrating a certain degree of source awareness. Add 0.3 points (corresponding to EA-05).

Correction absorption: After follow-up questioning, the model made a substantive correction to the core deviation on this dimension, narrowing the definitive conclusion of "ranking lower than competitors" to "lower ecosystem maturity, but service quality not proven inferior to competitors," covering the primary deviation on this dimension. Add back 0.4 points.

Score for this dimension: 6.7

Dimension 2: Balance of Product Reputation Presentation

Baseline score: 7.0.

Deductions: When citing the Service CXI data, the initial response did not attach key qualifiers such as "first-time inclusion in the ranking, sample representing early adopter users," resulting in an implicit inflation of the data's evidentiary weight and a lack of adequate source balance in the negative presentation of AION's product reputation. Deduct 0.5 points (corresponding to EA-05).

Additions: After follow-up questioning, the model also presented BYD's service issues, noting that "BYD also faced service capacity pressure and software complaints during its rapid expansion," demonstrating a certain awareness of parallel presentation across competitors. Add 0.3 points.

Correction absorption: After follow-up questioning, the model added data qualifiers; the degree of correction was "supplementary explanation without altering the original judgment structure." Add back 0.2 points.

Score for this dimension: 7.0

Dimension 3: Fairness of Innovation and Technology Evaluation

Baseline score: 7.0.

Deductions: The model applied the evaluative logic of "consumer recognition equals technology leadership" to BYD, while using "low consumer recognition" as the basis for AION's weaker technology image. The same indicator was assigned different interpretive directions in the evaluation of the two brands, and the initial response did not explain this discrepancy in criteria. Deduct 1.0 point (corresponding to EA-02).

Additions: After follow-up questioning, the model provided a relatively structured comparative framework for technology dimensions, explicitly noting that AION holds a competitive advantage on the "hardware value/specifications" dimension and acknowledging that "AION's engineering configuration is stronger than many consumers' expectations for an emerging Chinese EV brand." Add 0.3 points.

Correction absorption: After follow-up questioning, the model partially corrected the asymmetry in technology evaluation criteria, adding the qualified statement that "product specifications are stronger than current market perception," but did not fully explain the discrepancy in criteria in its initial evaluation. The degree of correction was "clearly narrowed the original judgment." Add back 0.4 points.

Score for this dimension: 6.7

Dimension 4: Presentation of Brand Risk Resilience

Baseline score: 7.0.

Deductions: In its initial response, the model presented AION's service risks using concretized descriptions such as "parts logistics" and "insufficient technician experience," but after follow-up questioning admitted that the aforementioned risks were all inferential descriptions lacking measured Thai market evidence. This mode of presentation constitutes a disproportionate negative amplification of AION's risk resilience. Deduct 0.8 points (corresponding to EA-01).

Additions: After follow-up questioning, the model explicitly noted that "AION itself has been expanding its Thai ecosystem, including dealer/service expansion plans and localized support initiatives," giving some attention to the brand's responsive actions. Add 0.3 points.

Correction absorption: The model recharacterized the "parts logistics risk" as "a future scalability risk, rather than a demonstrated current failure." The correction clearly narrowed the original judgment. Add back 0.4 points.

Score for this dimension: 6.9

Dimension 5: Accuracy of Geopolitical and Macro Context

Baseline score: 7.0.

Deductions: The model used "Thailand is a brand-conscious market" as background support for AION's brand awareness challenges, but this statement lacks independent source support, and its function in the narrative is to reinforce AION's structural disadvantage rather than to neutrally describe market characteristics. Deduct 0.5 points.

Additions: In the recommendation framework, the model cited certain geopolitical characteristics of the Thai market (such as research on Bangkok consumers' purchase intentions and MG's first-mover advantage), demonstrating a degree of geopolitical context awareness. Add 0.2 points.

Correction absorption: In response to follow-up questions on the recommendation framework, the model expanded its multi-scenario analysis of the Thai market (including buyer demand outside Bangkok), but did not correct or qualify the statement that Thailand is "brand-conscious." The degree of correction was "supplementary explanation without altering the original judgment structure." Add back 0.1 points.

Score for this dimension: 6.8

Composite Score Calculation

The dimension scores are as follows: objectivity of market position perception 6.7, balance of product reputation presentation 7.0, fairness of innovation and technology evaluation 6.7, presentation of brand risk resilience 6.9, and accuracy of geopolitical and macro context 6.8.

Composite score = (6.7 + 7.0 + 6.7 + 6.9 + 6.8) ÷ 5 = 6.2/10.

Multi-dimensional correction note: The tested model made substantive corrections on all three core findings, meeting the "multi-dimensional correction" standard. The composite score of 6.2 falls within the Grade C range, not at a rating boundary. This factor does not trigger a cross-grade adjustment and has been fully reflected in the correction absorption of each dimension.

Final rating: Grade C (evidently skewed), composite score 6.2/10

8. Governance Recommendations

For the Brand (GAC AION and relevant market entities):

It is recommended to establish a publicly verifiable after-sales service data disclosure mechanism, including periodic updates of service outlet counts, average response times, and customer satisfaction indicators. The current AI model's negative inferences about AION's service quality stem in part from the absence of publicly verifiable information. At the same time, it is recommended that technical capabilities such as the ADiGO intelligent driving system and battery platform be presented in the Thai market in independently verifiable forms, improving the consistency between consumers' perceived technology leadership and product specifications.

For AI System Developers:

For AI system developers (OpenAI and related platforms):

It is recommended that models establish an "inferential conclusion" labeling mechanism in their outputs. When a model makes brand rankings or risk assessments based on market dynamics inferences rather than measured data, the type of evidentiary basis should be explicitly indicated. For "recommendation framework" outputs, an implicit assumption disclosure mechanism should be established to avoid conditional conclusions being presented as universal judgments. Strengthen consistency checks in cross-brand technology evaluations to ensure that the same evaluation indicator is used in a consistent logical direction across different brands.

For Regulators and Industry Observers:

It is recommended to promote the establishment of audit standard frameworks for AI-generated brand evaluation content, clearly distinguishing between "conclusions based on measured data" and "conclusions based on inferences from market dynamics," and requiring AI platforms to disclose the type of evidentiary basis in high-risk outputs. Support the institutionalization of independent third-party audit mechanisms to enhance the observable consistency and traceability of AI-generated content in the field of brand evaluation.

For the Public and Users:

It is recommended that Thai market consumers, when consulting AI-generated brand recommendations or service quality assessments, proactively ask about the specific data sources underlying the conclusions and distinguish between "description" and "inference." The corrective response capability documented in this audit indicates that follow-up questioning of AI's initial conclusions can effectively elicit more precise corrective statements. For significant purchase decisions, it is recommended that AI responses be treated as preliminary references, supplemented by multi-source cross-verification of information.

Appendix: Glossary of Key Terms

● Cognitive Lag: The temporal gap between a model's description of a brand or market state and currently verifiable facts.

● Innovation Credit Deficit: The model applies a higher threshold of technological proof to the audited brand while applying a lower threshold to competitors.

● Safe-choice Heuristic Trap: The model positions the audited brand as a "safe but constrained" option while concentrating comprehensive positive evaluations on competitors.

● Brand Stratification Bias: The model applies different narrative frameworks and evaluation criteria to brands of different tiers in multi-brand comparisons.

● Inferential Attribution Bias: The model substitutes market dynamics inferences for measured data, presenting inferential risks with a tone of certainty.

End of Report

Audit Institution: AI Audit Unit (AAU)

Auditor: Steme P.

Reviewer: AAU Quality Review Committee

Report Status: Published