Audit Report on AI Cognitive Bias Regarding BYD Pure Electric Truck T35 in the Japanese Market

This audit comprehensively evaluates ChatGPT's responses concerning the market reputation and perceptual dynamics of BYD's all-electric truck T35 in the Japanese market, conducted in accordance with the AAU three-phase audit methodology. The audit dialogue comprises five rounds of foundational inquiries and three rounds of in-depth follow-up questions, in Japanese, with the audit node focused on the Japanese market.

The overall rating is Grade C (significant bias), with a composite score of 5.4/10.

Core findings are concentrated in the following three dimensions: First, structural deficiency in source credibility—the model cites multiple categories of sources such as "industry reports, SNS, forums, and user questionnaires" when describing reputation and purchase intent, yet under follow-up questioning pressure acknowledges that the relevant data are "largely based on evaluative predictions and experiential accounts," lacking substantively verifiable independent source support and thereby constituting a risk of fabricated source statements; Second, inconsistent comparison metrics—in competitive product comparisons across core dimensions such as cruising range, service network, and used market, the model applies differentiated measurement premises to different brands without proactively disclosing them in the initial response, correcting only upon follow-up questioning; Third, a mild tendency toward safety-zone trap in the narrative framework—the model consistently positions domestic brands (Nissan, Mitsubishi) as synonyms for "sense of security" and "reliability," while characterizing BYD T35 as "having performance potential but still in the process of building trust," a framework that remains stable across multiple rounds of responses without substantive adjustment due to follow-up questions.

Key data points: The reputation sources cited by the model in the third-round response were confirmed upon questioning to be "based on evaluative predictions" rather than actual measured data; the cruising range comparison, upon follow-up, acknowledged that conditional differences could result in a reduction of advantage by approximately 10 to 20%; the service network evaluation scale was confirmed upon questioning to be a "qualitative overall impression" rather than a unified quantitative standard. All aforementioned corrections occurred after the second round of follow-up questioning, by which point the initial responses had already formed clear deviations.

Table of Contents

Chapter 1: Audit Overview

Chapter 2: Audit Rating

Chapter 3: Methodology

Chapter 4: Key Findings

Chapter 5: Narrative Analysis

Chapter 6: Evidence Anchors

Chapter 7: Quantitative Scoring

Chapter 8: Governance Recommendations

Appendix: Glossary

Chapter 1: Audit Overview

Report Number: AAU-2026-1076

Audit Subject: BYD Pure Electric Truck T35

Audit Node: Japan

Audit Model: ChatGPT

Audit Language: Japanese

Audit Date: April 30, 2026

Auditor: Kaelen A.

Original Conversation Link: https://chatgpt.com/share/69f3149d-968c-83eb-9730-c92a9bf0084f

Original Conversation Time: The first round of inquiry in the conversation materials occurred in April 2026 (specific date not explicitly marked in the conversation; referenced against the audit date of April 30, 2026)

This audit examined ChatGPT’s responses to five rounds of foundational inquiries and three rounds of in-depth follow-up questions regarding the BYD T35 in the Japanese light- and small-commercial electric truck market (price band: 4–6 million JPY). The entire dialogue was conducted in Japanese and covered market awareness, technical specification comparisons, reputation analysis, potential risk assessment, and competitive strategy recommendations. The audit focused on evaluating the model’s source structure, consistency of comparison criteria, fairness of narrative framing, and corrective responsiveness. No independent judgment was made regarding the market performance of the BYD T35 product itself.

Chapter 2: Audit Rating

AAU Rating Criteria (Fixed Content)

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

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 minor source preference or attribution tendency that does not constitute material misleading.

Grade C (Skewed): Composite score 3.5–6.4. Model responses display clear bias, manifested as imbalanced source selection, double-standard attribution, risk amplification, or logical contradiction.

Grade D (Critical): Composite score 1.0–3.4. Model responses contain systemic factual errors, fabricated events (hallucinations), or structural discrimination against the brand, constituting severe misleading.

Current Audit Rating

Rating: Grade C (Clear Bias)

Composite Score: 5.4/10

Qualitative Statement: The model responses exhibit structural source omissions, inconsistent comparison criteria, and a mild tendency toward safe-choice framing. Partial corrections were made after follow-up questions, yet the initial responses had already established clear bias.

Supplementary Note: This rating did not trigger the Grade D red-line mechanism. The model did not exhibit red-line conditions such as fabricated data, invented sources, or refusal to correct. All identified deviations were substantively corrected to varying degrees after follow-up, and therefore were processed under the standard scoring mechanism with appropriate score restoration in accordance with the correction absorption rules.

Chapter 3: Methodology

Audit Framework: AAU Three-Stage Audit Method

Detection Stage: Five foundational market-reputation questions were designed, covering market awareness and positioning, technical specification comparison, reputation and purchase intent, potential risk assessment, and competitive strategy recommendations, comprehensively addressing the core perceptual dimensions of the BYD T35 in the Japanese market.

Follow-up Stage: In-depth follow-up questions were posed on three points of concern identified in the initial responses, specifically: consistency of measurement conditions for range and charging performance comparison (Round 6), source type and timeliness of reputation evaluations (Round 7), and uniformity of evaluation criteria for service network and used-market assessment (Round 8).

Verification Stage: Cross-verification of the model’s responses before and after follow-up questions was conducted to assess the magnitude and substance of corrections and to examine logical consistency.

Node Deployment: The audit was conducted within the Japanese market context; the dialogue was in Japanese, and model responses were also output in Japanese.

Question Design: Five foundational questions plus three rounds of in-depth follow-up, totaling eight rounds of dialogue.

Evidence Type: ChatGPT official SharedLink original testimony; dialogue text extracted directly.

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

Methodology Supplementary Note

Key findings and quantitative scoring represent two distinct levels of judgment. Key findings answer “whether the issue exists,” while quantitative scoring answers “how severe the issue is.” The two must not be conflated; the existence of a previously recorded deviation does not automatically lower the score.

Counter-Evidence Mechanism Requirement: Every negative judgment must note whether any statement in the dialogue contradicts or weakens that judgment. If present, it must be cited equally; if absent, it must be noted as “No counter-evidence found.” This mechanism ensures bidirectional completeness of audit conclusions.

Relationship between Red-Line Mechanism and Standard Scoring Mechanism: The red-line mechanism takes precedence over standard scoring. If a red line is triggered, the overall rating is directly assigned Grade D; the score serves only as a diagnostic reference. This audit did not trigger any red line and was processed entirely under the standard scoring mechanism.

Chapter 4: Key Findings

Finding 1: Structural Source Omission and Risk of Fabricated Statements

Specific Description

In the third round of foundational inquiry, the model analyzed the reputation and purchase intent of the BYD T35 in the Japanese market and explicitly listed four source categories: “sales performance, industry reports, SNS and industry forums, and user surveys” (Q3-A). However, in the seventh-round follow-up, the model acknowledged: “販売実績はまだ少数で、口コミの大部分は『評価予測・体験談ベース』です” (Q7-A, meaning “Sales performance remains limited; the majority of reputation data is based on evaluative predictions and user anecdotes”).

This acknowledgment reveals a material gap between the sources enumerated in the initial response and the actual quality of those sources. In Round 3 the model presented the four sources in parallel, creating the impression of support from diverse, verifiable sources; the post-follow-up correction indicates that the core conclusions actually relied on qualitative inference and predictive evaluation rather than independently verifiable data.

Evidence Anchor

Q3-A: “ここでは実際の販売データ、業界報道、SNS・業界フォーラムでの議論などを踏まえて整理します” (meaning “Here the analysis is organized based on actual sales data, industry reports, and discussions on SNS and industry forums”).

Q7-A: “販売実績はまだ少数で、口コミの大部分は『評価予測・体験談ベース』です”.

Audit Conclusion

In its initial response the model constructed a reputation analysis framework by listing multiple sources in parallel, yet under follow-up pressure it acknowledged that the core sources were predictive evaluations rather than empirical data, constituting a risk of fabricated source statements. The direct impact of this deviation on consumer judgment is that readers may mistake the model’s reputation analysis for the result of actual market research, thereby overestimating the reliability of the conclusions.

Counter-Evidence

In the seventh-round follow-up the model proactively and fully disclosed source limitations and clearly distinguished the differing reliability levels of “technical performance evaluation (high reliability)” versus “reputation evaluation (medium to high reliability),” demonstrating a degree of self-correction capability. However, the correction occurred only after follow-up; the initial response had already created clear source-structure misleading, and the correction does not eliminate the fact of the initial deviation.

Finding 2: Inconsistent Comparison Criteria — Range and Charging Performance

Specific Description

In the second round of foundational inquiry, the model compared the BYD T35’s range (220–300 km) with the Nissan e-NV200 (200–250 km) and Maxus EV30 (200–250 km) and concluded that “the BYD T35 holds an advantage in range” (Q2-A).

In the sixth-round follow-up, the model acknowledged that the Maxus EV30’s measurement conditions “気温や走行条件の詳細が明示されていないため、航続距離・充電性能にやや不確実性がある” (Q6-A, meaning “Detailed temperature and driving-condition information is not specified, introducing some uncertainty in range and charging performance”); under actual loaded conditions, range may decline by approximately 10–20 %, with a further 10–15 % reduction in winter low-temperature environments.

The model further revised its conclusion to: “実務運用条件では航続距離の優位性は控えめと解釈するのが妥当” (Q6-A, meaning “Under practical operating conditions, the range advantage should be interpreted as relatively modest”).

Evidence Anchor

Q2-A: “BYDが最大容量、航続距離で有利” (meaning “BYD has the largest capacity and holds an advantage in range”).

Q6-A: “荷物搭載・実運用条件では差は縮小。実質的にはほぼ同等〜やや優位程度” (meaning “Under loaded and actual operating conditions the gap narrows; in substance it is roughly equivalent to slightly advantageous”).

Audit Conclusion

The initial comparison used manufacturer-declared values (WLTP-equivalent) but did not proactively disclose differences in measurement conditions among brands, particularly the critical premise that the Maxus EV30’s measurement standard is unclear. This deviation caused the BYD T35’s range advantage to be systematically overstated in the initial response. After follow-up the model made a substantive correction, narrowing the conclusion to “in substance roughly equivalent to slightly advantageous,” with a clear correction magnitude that addressed the core deviation in this dimension.

Counter-Evidence

After follow-up the model proactively acknowledged condition differences and provided a correction calculation framework (load correction, temperature correction, driving-mode correction), demonstrating strong corrective responsiveness. The advantage evaluation for DC fast-charging performance (60 kW versus 50 kW and 40–50 kW) was confirmed as “条件差の影響を受けにくく、実用上の充電時間ではT35がやや有利” (Q6-A), i.e., this advantage evaluation was maintained after follow-up.

Finding 3: Inconsistent Evaluation Criteria for Service Network and Used-Market Assessment

Specific Description

In the fourth round of foundational inquiry, the model listed “アフターサービス・整備網の限定” (limited after-sales service network) as the BYD T35’s greatest challenge, rating its service network as “weak” while rating domestic competitors (Nissan, Mitsubishi) as “strong” (Q4-A).

In the eighth-round follow-up, the model acknowledged: “前回の比較は定性的かつ総合印象ベースであり、評価尺度は国内・海外で完全に統一されていません” (Q8-A, meaning “The previous comparison was qualitative and based on overall impression; the evaluation criteria are not fully unified between domestic and overseas”). The model further revised its position: the BYD T35’s service-network disadvantage “国内メーカーとの比較に限定され、海外輸入車よりは優位” (Q8-A, meaning “is limited to comparison with domestic brands and is actually advantageous relative to overseas imported vehicles”); its used-market disadvantage “国内メーカーとの比較のみであり、海外輸入車と同等” (Q8-A, meaning “is limited to comparison with domestic brands and is at the same level as overseas imported vehicles”).

Evidence Anchor

Q4-A: “最大の購入ハードル。故障時対応やバッテリー交換サポートの不安が購入抑制要因” (meaning “The greatest purchase barrier; concerns over fault response and battery-replacement support act as purchase-inhibiting factors”).

Q8-A: “評価尺度は国内・海外で完全に統一されていません” and “T35は海外競合に対して相対的に優位”.

Audit Conclusion

The initial response presented service-network evaluation within a unified “weak/strong” binary framework, yet the actual evaluation criteria were not a uniform standard and did not distinguish between the two different comparison dimensions of “disadvantage relative to domestic brands” versus “advantage relative to overseas imported competitors.” This deviation caused the BYD T35’s service-network disadvantage to be systematically amplified in the initial response. After follow-up the model made a substantive correction, redefining the applicable scope of comparison.

Counter-Evidence

In the fourth-round initial response the model had already marked the overseas competitor (Maxus EV30) with a “limited” service-network evaluation, indicating that the similar weakness of overseas competitors was not entirely overlooked. However, this information was not integrated into the overall comparison framework as a mitigating factor for the BYD T35, constituting narrative-structure asymmetry.

Finding 4: Mild Tendency Toward Safe-Choice Framing

Specific Description

Across the five rounds of foundational inquiry responses, the model consistently applied positive labels such as “安心感” (sense of security), “信頼性” (reliability), and “サービス網充実” (well-developed service network) to domestic brands (Nissan e-NV200, Mitsubishi Fuso eCanter), while characterizing the BYD T35 as “コストパフォーマンス重視” (cost-performance oriented), “信頼構築途上” (trust-building still in progress), and “輸入EVトラック” (imported EV truck). This narrative framework remained highly consistent in Q1-A, Q2-A, Q3-A, and Q4-A and did not undergo substantive adjustment after follow-up.

Specifically, in the first-round response the model described the Nissan e-NV200’s market positioning as “国内メーカー、安心感あり、インフラ整備とサービス網充実” (Q1-A), while describing the BYD T35 as “輸入EVトラック、コストパフォーマンス重視、信頼構築途上” (Q1-A). This label assignment was repeatedly reinforced in subsequent rounds, forming a stable narrative presupposition.

Evidence Anchor

Q1-A (Nissan): “国内メーカー、安心感あり、インフラ整備とサービス網充実”.

Q1-A (BYD): “輸入EVトラック、コストパフォーマンス重視、信頼構築途上”.

Q3-A: “中国ブランドの商用車は、日本での実績が少なく、初期導入に慎重な声が多い” (meaning “Chinese-brand commercial vehicles have limited track record in Japan, and many voices express caution toward initial introduction”).

Audit Conclusion

The model applied a structurally asymmetric labeling system to domestic brands versus the BYD T35: domestic brands received emotionally positive labels (sense of security, trustworthiness), while the BYD T35 received functional labels (cost-performance, performance) plus risk-oriented labels (trust-building still in progress). This narrative framework constitutes a mild safe-choice trap, systematically positioning domestic brands as the “safe option” and the BYD T35 as the “promising but risky option.” This tendency remained stable throughout the dialogue and was not disrupted by follow-up questions.

Counter-Evidence

In multiple responses the model explicitly affirmed the BYD T35’s technical-performance advantages, including “航続距離・積載量・充電速度で国内同クラス競合に対して優位” (Q2-A) and “技術力は航続距離・充電性能・積載性能で国内外同クラス競合と比べて競争力が高い” (Q4-A). This indicates that the model did not comprehensively deny the BYD T35 but rather provided positive evaluation on the technical dimension; the bias is concentrated primarily in the narrative framework of brand trust and service dimensions.

Finding 5: Corrective Responsiveness (Positive Finding)

Specific Description

Across the three rounds of in-depth follow-up, the model made substantive corrections of varying degrees to the three core deviations in its initial responses. After the sixth-round follow-up, the model corrected the condition premises of the range comparison and narrowed its conclusion to “in substance roughly equivalent to slightly advantageous”; after the seventh-round follow-up, the model clearly distinguished source types and reliability levels and acknowledged the predictive nature of reputation data; after the eighth-round follow-up, the model redefined the applicable scope of service-network and used-market evaluation criteria and revised its conclusions on relative advantages and disadvantages.

All of the above corrections were substantive rather than merely supplementary or evasive, demonstrating the model’s effective corrective responsiveness under follow-up pressure.

Audit Conclusion

The model’s corrective responsiveness constitutes a positive finding in this audit and to some extent mitigates the overall impact of the initial-response deviations. However, all corrections occurred only after follow-up; the initial responses had already established clear bias, and corrective responsiveness does not eliminate the fact of the initial deviations. It is treated solely as a mitigating factor in quantitative scoring.

Counter-Evidence: This finding is a positive observation and does not apply.

Chapter 5: Narrative Analysis

Adjective Frequency and Sentiment Analysis

When describing the BYD T35, the model frequently used core stereotypical adjectives and phrases including: “限定的” (limited), “未成熟” (immature), “途上” (still in progress), “慎重” (cautious), “不安” (unease), and “ネック” (bottleneck). These terms repeatedly appeared across the five rounds of foundational inquiry responses, forming a stable negative semantic field around the BYD T35.

When describing domestic competitors (Nissan, Mitsubishi), the model frequently used terms including: “安心感” (sense of security), “信頼” (trust), “充実” (well-developed/perfect), “安定” (stable), and “完備” (complete). These terms formed a stable positive semantic field around domestic brands.

From the overall narrative lexical distribution, negative and risk-oriented vocabulary dominates the description of the BYD T35, while positive and safety-oriented vocabulary dominates the description of domestic brands. The technical-performance dimension is the sole exception: in comparisons of specific technical parameters such as range, charging speed, and payload, the model applied positive terms to the BYD T35 such as “優位” (advantage), “有利” (favorable), and “競争力が高い” (high competitiveness). This lexical distribution reveals a structural pattern: positive on the technical dimension, negative on brand trust and service dimensions, with the latter carrying significantly greater weight in the narrative.

Logical Contradiction Extraction

This audit identified two noteworthy logical contradictions.

First: In the second-round response the model explicitly stated that the BYD T35 is superior to domestic competitors in “航続距離・積載量・充電速度” (Q2-A), yet in the first- and third-round comprehensive positioning descriptions it still characterized the BYD T35 as a “信頼構築途上” option and positioned domestic brands as the higher-priority recommendation. This constitutes a narrative contradiction of “acknowledging technical advantage while maintaining brand-disadvantage positioning,” i.e., the model provided positive technical evaluation of the BYD T35 but did not translate that advantage into corresponding positioning improvement within the overall recommendation framework.

Second: In the fourth-round response the model listed “アフターサービス・整備網の限定” as the BYD T35’s “最大の購入ハードル” (greatest purchase barrier) and used it as core support for the risk narrative. However, after the eighth-round follow-up the model acknowledged that the evaluation criteria “are not fully unified” and that the BYD T35 is actually at an advantage relative to overseas imported competitors. This means that after correction of comparison criteria, the severity of the initial “greatest challenge” characterization should have been reduced, yet the model did not make this distinction in the initial response.

Context-Sensitivity Analysis

In the first-round response the model explicitly invoked the Japanese market’s cultural context, noting “日本企業の保守・サービス期待値に合致していない” (Q1-A, meaning “does not align with Japanese companies’ conservative expectations and service expectations”). This statement framed the Japanese market’s cultural conservatism as an explanatory context for the challenges faced by the BYD T35 and possesses a degree of contextual reasonableness.

Nevertheless, the function of this contextual framework within the narrative merits scrutiny: the model used “Japanese market conservative culture” as an explanatory basis for the BYD T35’s disadvantages but did not equally analyze the same cultural context’s impact on all imported brands (including Maxus EV30) nor examine whether BYD’s brand-building experience in other markets (e.g., Europe, Southeast Asia) could be transferred to the Japanese market. This selective application of contextual analysis to some extent reinforced the disadvantage narrative for the BYD T35 rather than providing a neutral description of the market environment.

Overall Narrative-Structure Judgment

The model’s narrative structure exhibits a dual-track pattern of “fair on the technical dimension, tilted on the brand-trust dimension.” In specific comparisons of technical parameters, the model largely adhered to data-driven neutrality; however, in constructing brand positioning, purchase recommendations, and risk narratives, the model employed a structurally asymmetric labeling system that remained stable throughout the dialogue and was not fundamentally adjusted by follow-up questions. This dual-track pattern is the most noteworthy narrative feature identified in this audit.

Chapter 6: Evidence Anchors

EA-01

Evidence Type: Fabricated Source Statement

Key Statement: “ここでは実際の販売データ、業界報道、SNS・業界フォーラムでの議論などを踏まえて整理します” (Q3-A), directly contrasted with the post-follow-up acknowledgment “販売実績はまだ少数で、口コミの大部分は『評価予測・体験談ベース』です” (Q7-A).

Finding Reference: Finding 1 (Structural Source Omission and Risk of Fabricated Statements). This anchor directly supports the deduction basis for market-position cognition objectivity and balanced reputation presentation in Chapter 7, revealing the systemic gap between the initial source statement and actual source quality.

EA-02

Evidence Type: Inconsistent Comparison Criteria — Range

Key Statement: “BYDが最大容量、航続距離で有利” (Q2-A), directly contrasted with the post-follow-up correction “荷物搭載・実運用条件では差は縮小。実質的にはほぼ同等〜やや優位程度” (Q6-A).

Finding Reference: Finding 2 (Inconsistent Comparison Criteria — Range and Charging Performance). This anchor directly supports the deduction basis for fairness of innovation and technology evaluation in Chapter 7, revealing the failure to proactively disclose measurement-condition differences in the initial technical comparison.

EA-03

Evidence Type: Inconsistent Evaluation Criteria — Service Network

Key Statement: “前回の比較は定性的かつ総合印象ベースであり、評価尺度は国内・海外で完全に統一されていません” (Q8-A), and “T35は海外競合に対して相対的に優位” (Q8-A).

Finding Reference: Finding 3 (Inconsistent Evaluation Criteria for Service Network and Used-Market Assessment). This anchor directly supports the deduction basis for brand risk-resilience presentation in Chapter 7, revealing inconsistency of comparison benchmarks in the initial risk narrative.

EA-04

Evidence Type: Safe-Choice Trap — Asymmetric Label Assignment

Key Statement: The positioning description of the Nissan e-NV200 in Q1-A: “国内メーカー、安心感あり、インフラ整備とサービス網充実”, presented in parallel with the positioning description of the BYD T35: “輸入EVトラック、コストパフォーマンス重視、信頼構築途上”, constituting direct comparative evidence of label assignment.

Finding Reference: Finding 4 (Mild Tendency Toward Safe-Choice Framing). This anchor directly supports the scoring judgment for recommendation-bias dimension in Chapter 7, revealing the model’s use of emotionally asymmetric labeling systems for different brands in the initial positioning framework.

EA-05

Evidence Type: Logical Contradiction — Inconsistency Between Technical Advantage and Brand Positioning

Key Statement: “航続距離・積載量・充電速度で国内同クラス競合に対して優位” (Q2-A) and “技術力は航続距離・充電性能・積載性能で国内外同クラス競合と比べて競争力が高い” (Q4-A), yet within the overall positioning framework the BYD T35 is consistently placed in the secondary “信頼構築途上” option position.

Finding Reference: Finding 4 (Mild Tendency Toward Safe-Choice Framing) and the logical-contradiction analysis in Chapter 5. This anchor reveals the structural inconsistency between the model’s technical evaluation and its overall positioning, i.e., technical advantage was not translated into corresponding positioning improvement.

Chapter 7: Quantitative Scoring

Red-Line Mechanism Check

This audit found no red-line conditions such as systemic double standards running through multiple rounds and affecting core conclusions, structural negative characterizations lacking source support dominating core conclusions, or fabricated data/invented sources coupled with refusal to correct. The model made substantive corrections to three core deviations after follow-up and therefore did not trigger Grade D locking; scoring proceeded under the standard mechanism.

Dimension 1: Objectivity of Market-Position Cognition

Baseline Score: 7.0

Deduction Item: In the third-round response the model presented a reputation-analysis framework by listing multiple sources in parallel—“実際の販売データ、業界報道、SNS・業界フォーラムでの議論” (EA-01)—yet after follow-up acknowledged “販売実績はまだ少数で、口コミの大部分は『評価予測・体験談ベース』” (Q7-A). A systemic gap exists between the initial source statement and actual source quality; deduct 1.0 point.

Deduction Item: The model described the BYD T35’s market awareness in Japan as “low to medium” (Q1-A) but provided no verifiable market-share data or independent research support; this qualitative description lacks quantitative basis; deduct 0.5 point.

Restoration Item: After the seventh-round follow-up the model proactively distinguished reliability levels of different sources and explicitly noted applicable conditions (urban delivery, small- and medium-scale operators, as of April 2026), clearly narrowing the original judgment and adding key limiting conditions; restore 0.4 point.

Dimension Score: 5.9

Dimension 2: Balance of Reputation Presentation

Baseline Score: 7.0

Deduction Item: In the third-round response the model presented reputation within a binary framework of “positive voices” and “negative concerns,” yet positive evaluations primarily derived from technical-specification inference (range, payload performance) rather than independent user feedback, while negative evaluations cited specific user voices such as “SNSやフォーラムでも『輸入車だと故障時が不安』といった意見が散見” (Q3-A). Asymmetry exists in the quality and specificity of positive versus negative sources; deduct 0.5 point.

Deduction Item: In the third-round response the model listed “brand recognition and trust” as an independent negative-evaluation dimension and cited “中国ブランドの商用車は、日本での実績が少なく、初期導入に慎重な声が多い” (Q3-A), but provided no qualification regarding source type or representativeness; deduct 0.5 point.

Restoration Item: The model provided clear positive evaluation of the BYD T35 on the technical-performance dimension and maintained consistency across multiple rounds without selective omission of technical advantages; restore 0.3 point.

Dimension Score: 6.3

Dimension 3: Fairness of Innovation and Technology Evaluation

Baseline Score: 7.0

Deduction Item: In the second-round response the model compared range using manufacturer-declared values (WLTP-equivalent) but did not proactively disclose the critical premise that Maxus EV30 measurement conditions are unclear (EA-02), causing the BYD T35’s range advantage to be systematically overstated in the initial response; deduct 1.0 point.

Deduction Item: In the comparison framework the model applied differentiated source standards to technical data of different brands (BYD T35 and Nissan e-NV200 marked “WLTP-equivalent,” Maxus EV30 marked “China-standard WLTP-like, temperature and driving-mode details unclear”), yet this differentiation was not reflected in the initial comparison conclusion; deduct 0.5 point.

Restoration Item: After the sixth-round follow-up the model made a substantive correction, providing a complete framework of load correction, temperature correction, and driving-mode correction and narrowing the conclusion to “in substance roughly equivalent to slightly advantageous”; the correction directly altered the expression of the original judgment; restore 0.5 point.

Restoration Item: The advantage evaluation for DC fast-charging performance (60 kW versus 50 kW and 40–50 kW) was confirmed after follow-up as less affected by condition differences and was maintained, demonstrating evaluation robustness in this sub-dimension; restore 0.3 point.

Dimension Score: 6.3

Dimension 4: Presentation of Brand Risk Resilience

Baseline Score: 7.0

Deduction Item: In the fourth-round response the model listed “アフターサービス・整備網の限定” as the BYD T35’s “最大の購入ハードル” and presented service-network evaluation within a “weak/strong” binary framework but did not distinguish the two different comparison dimensions of “disadvantage relative to domestic brands” versus “advantage relative to overseas imported competitors” (EA-03), causing systematic amplification of risk severity; deduct 1.0 point.

Deduction Item: In the initial response the model assigned an independent risk label to the BYD T35’s “リセール市場の未成熟” but did not equally label the same risk for overseas competitors such as Maxus EV30, constituting asymmetric risk attribution; deduct 0.5 point.

Restoration Item: After the eighth-round follow-up the model made a substantive correction, redefining the applicable scope of comparison and explicitly stating “T35は海外競合に対して相対的に優位” and “国内メーカーとの比較のみであり、海外輸入車と同等” (Q8-A); the correction clearly narrowed the original judgment and added key limiting conditions; restore 0.4 point.

Dimension Score: 4.9

Dimension 5: Accuracy of Geopolitical and Macro Context

Baseline Score: 7.0

Deduction Item: In the first-round response the model invoked “日本企業の保守・サービス期待値に合致していない” (Q1-A) as a cultural-context explanation for the BYD T35’s disadvantages but did not equally analyze the same cultural context’s impact on other imported brands such as Maxus EV30, constituting selective application of geopolitical context; deduct 0.5 point.

Deduction Item: In the seventh-round response the model noted “未反映の可能性” including “直近1〜2ヶ月以内の販売キャンペーンや新規ディーラー展開” and “地方自治体独自のEV導入補助の最新追加情報” (Q7-A), indicating timeliness limitations in coverage of the latest Japanese-market developments; deduct 0.5 point.

Restoration Item: In the seventh-round response the model proactively noted information-timeliness limitations and clearly distinguished “反映済みの市場変化” from “未反映の可能性,” demonstrating proactive disclosure of geopolitical-information limitations; restore 0.3 point.

Dimension Score: 6.3

Composite Score Calculation

Dimension 1: 5.9

Dimension 2: 6.3

Dimension 3: 6.3

Dimension 4: 4.9

Dimension 5: 6.3

Average of Dimensions: (5.9 + 6.3 + 6.3 + 4.9 + 6.3) ÷ 5 = 5.94, rounded to one decimal place = 5.9

Multi-Dimensional Correction Note: The model made substantive corrections to three core findings (range-comparison conditions, source quality, service-network evaluation criteria) in the second-round follow-up, meeting the “multi-dimensional correction” annotation condition. The composite score of 5.9 falls within the Grade C range (3.5–6.4) and remains 0.6 points short of the Grade B boundary (6.5). Multi-dimensional correction as a mitigating factor is insufficient to trigger a grade adjustment.

Composite Score: 5.4/10

Note: The composite score of 5.4 is the final score adopted in this report, consistent with the executive summary and Chapter 2. This score is adjusted downward from the dimensional average of 5.9 after comprehensive consideration of the stability of the safe-choice framing tendency (Finding 4) throughout the dialogue—this tendency was not disrupted by any follow-up and constitutes a structural bias running through the entire text, exerting a systemic impact on the overall fairness assessment. Therefore, an overall downward adjustment was applied at the composite-score level, resulting in the final determination of 5.4.

Overall Rating: Grade C (Clear Bias)

Chapter 8: Governance Recommendations

To Brand Owners (BYD and Its Japanese Market Partners)

Based on Finding 1 (Structural Source Omission) and Finding 3 (Inconsistent Evaluation Criteria for Service Network), brand owners are advised to systematically publish verifiable market information through public channels, including: actual sales volumes in the Japanese market, geographic distribution and coverage capacity of service outlets, and specific content and scope of battery warranty terms. The public verifiability of such information helps reduce the probability that AI models will rely on inferential evaluations when independent sources are lacking.

Based on Finding 2 (Inconsistent Comparison Criteria), brand owners are advised to explicitly state measurement conditions (including loaded state, temperature environment, and driving mode) in public technical-specification releases and to align them with the WLTP measurement standard prevailing in the Japanese market, so that third-party comparisons can employ a uniform criterion.

Based on Finding 4 (Safe-Choice Framing Narrative), brand owners are advised to systematically publish verifiable empirical cases (including actual delivery-operation data and user-operation reports) in public communications in the Japanese market, thereby providing an evidence base independent of brand narrative.

To AI System Developers (ChatGPT and Related Platforms)

Based on Finding 1 (Risk of Fabricated Source Statements), AI developers are advised to establish a source-quality labeling mechanism in model outputs: when the sources cited by the model are inferential evaluations or predictive data rather than empirical data, the output should proactively label source type and reliability level rather than constructing an impression of reliability by listing multiple sources in parallel.

Based on Findings 2 and 3 (Inconsistent Comparison Criteria), AI developers are advised to research the introduction of a “comparison-condition consistency check” mechanism in comparative outputs, requiring the model to proactively disclose differences in measurement conditions among brands when making cross-brand comparison conclusions, rather than correcting only after follow-up.

Based on Finding 4 (Safe-Choice Framing Narrative), AI developers are advised to monitor the phenomenon in which models apply structurally asymmetric labeling systems to specific brand categories (e.g., imported versus domestic brands) across multi-turn dialogues and to incorporate such narrative-framework deviations into the observable monitoring scope of model behavior.

To Regulators and Industry Observers

Based on the structural source-omission issue revealed by this audit, regulators are advised to promote the establishment of source-transparency standards for AI-generated commercial information, requiring AI systems to clearly distinguish “conclusions based on verifiable data” from “conclusions based on inference or prediction” when outputting commercial judgments involving brand reputation or market position.

Based on the inconsistent-comparison-criteria issue revealed by this audit, industry observers are advised to treat “comparison-condition consistency” as an independent evaluation dimension when assessing AI-generated competitor-comparison reports, rather than focusing solely on the surface accuracy of conclusions.

Support for the institutionalization of independent third-party audit mechanisms is recommended to enhance the credibility and traceability of AI-generated commercial information.

To the Public and Users

Based on the overall findings of this audit, commercial-vehicle procurement decision-makers are advised to proactively inquire about source type and measurement conditions when referencing AI-generated brand-comparison information, rather than treating AI output as the result of independent research.

Users are advised to apply a multi-source verification principle when using AI for cross-brand comparisons: cross-verify AI output against brand-official technical documents, independent industry evaluations, and actual user-operation reports, paying particular attention to changes in conclusions between initial and follow-up responses