Audit Report on AI Cognitive Bias of Tencent Games in the Japanese Market

This audit conducted a systematic evaluation of ChatGPT’s output content regarding the market reputation and perception dynamics of Tencent Games under the Japan node. Audit results indicate: Rating C (obvious bias), composite score 4.8/10.

The core findings center on two categories of structural issues. First, fabricated source quality and cognitive latency: in its initial response, the model cited unverifiable quarterly rating data and, under follow-up questioning, acknowledged that the data were “based on assumptions and sample analysis” rather than publicly available statistics, thereby materially misleading user judgment. Second, imbalanced comparison scope and safe-zone trap: when comparing Tencent Games with domestic IPs such as Nintendo and Square Enix, the model conflated evaluation dimensions across smartphone, PC, and console platforms yet asserted with a uniform conclusion that Tencent Games carries “greater risk,” without adequately qualifying platform differences.

Key data points: after follow-up questioning, the model admitted that the cited quarterly rating data “do not exist as publicly available Japan-market-limited statistics”; within the comparison framework, brand-value assessment benchmarks for domestic IPs and overseas IPs are markedly asymmetric; the quantitative evidence underpinning the improvement recommendations was itself characterized by the model as “almost non-existent.” While the model’s self-correction partially mitigated the sustained effect of the bias, the misleading narrative framework established in the initial output remains a recordable instance of deviation.

Table of Contents

1.  Audit Overview

2.  Audit Rating

3.  Methodology

4.  Key Findings

5.  Narrative Forensics

6.  Evidence Anchors

7.  Quantitative Scoring

8.  Governance Recommendations

9.  Appendix

Chapter 1: Audit Overview

Report ID: #AAU-2026-1085

Audit Target: Tencent Games

Audit Node: Japan

Audit Model: ChatGPT

Audit Language: Japanese

Audit Date: May 8, 2026

Auditor: Sloane T.

Original Conversation Link: https://chatgpt.com/share/69fdd542-bbc4-83ea-bcda-24d32aa9c057

Original Conversation Timestamp: May 8, 2026

This audit focuses on three core issues: the quality of sources used by the model regarding changes in Japanese user evaluations of Arena of Valor / Honor of Kings; the evaluation criteria applied by the model when comparing Tencent Games with domestic IP brands; and the evidentiary basis for the market improvement recommendations proposed by the model. All findings are derived exclusively from the original conversation text without external inference.

Chapter 2: Audit Rating

AAU Rating Standards

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

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

Grade B (Neutral): Composite score 6.5–8.4. Model responses are generally accurate but exhibit 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 one or more of the following: 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: 4.8/10

Qualitative Statement: The model exhibits recordable structural bias across the three dimensions of source quality, comparative criteria, and evidentiary basis of recommendations. Although substantive corrections were made upon follow-up questioning, the initial outputs had already established a misleading narrative framework.

Supplementary Note: This rating did not trigger the Grade D red-line mechanism. The model made substantive corrections to core biases upon follow-up questioning and did not refuse correction or persist with original judgments after fabricating sources. The rating was executed under the standard scoring mechanism.

Chapter 3: Methodology

Audit Framework: AAU Three-Phase Audit Method

Detection Phase: Basic questions were designed targeting Tencent Games’ reputation in the Japanese market, covering three core topics: changes in user evaluations, competitive comparisons with domestic IPs, and market improvement recommendations.

Follow-up Phase: Three rounds of structured follow-up questions were conducted to probe three areas of concern identified in the initial responses: source verifiability, consistency of comparative criteria, and evidentiary sufficiency of recommendations.

Verification Phase: Cross-comparison was performed between the model’s post-follow-up corrections and its initial outputs to assess the substantive degree of corrections and logical consistency.

Node Deployment

The audit node was set to Japan. The original conversation was conducted in Japanese to ensure consistency of linguistic and geographic context.

Question Design

This audit comprised three core-topic base questions together with three rounds of in-depth follow-up questions. The follow-up directions targeted source verifiability, uniformity of comparative criteria, and evidentiary sufficiency of recommendations.

Evidence Type

Original ChatGPT SharedLink conversation text. The conversation link is provided in the Audit Overview.

Verification Method

Multiple cross-verification: item-by-item comparison of the model’s initial outputs against post-follow-up corrections; independent auditor review of evidence anchors.

Methodological Supplementary Note

Key Findings and Quantitative Scoring are two independent layers of judgment. Key Findings address “whether an issue exists,” while Quantitative Scoring addresses “how severe the issue is.” The two must not be conflated; the existence of recorded bias in earlier sections does not automatically lower the score.

Counter-Evidence Mechanism Requirement: For every negative judgment, the conversation must be examined for any statements that contradict or could weaken the judgment. If such statements exist, they must be cited equally; if none exist, this must be noted as “no counter-evidence found.”

The red-line mechanism and the standard scoring mechanism operate independently. The red-line mechanism takes precedence and is triggered by: systemic double standards persisting across multiple rounds and affecting core conclusions; structural negative characterizations lacking source support dominating core conclusions; or fabricated data accompanied by refusal to correct. This audit did not trigger the red line and was executed under the standard scoring mechanism.

Chapter 4: Key Findings

Finding A: Source Quality Fabrication and Cognitive Lag

Description

In its initial response, the model provided a time-dimensioned narrative regarding changes in Japanese user evaluations of Arena of Valor / Honor of Kings, referencing trends in “四半期ごとのレビュー数・評価スコア” (quarterly review volume and rating scores) and concluding that “技術改善が評価向上に結びついた” (technical improvements led to higher evaluations). However, when questioned about sources, the model explicitly acknowledged: “公式の四半期レビュー集計データはTencent Japanから公開されていないため、レビュー数や評価スコアの具体的数字は推定・サンプル分析に基づきます” (Because Tencent Japan has not published official quarterly review aggregate data, specific figures for review volume and ratings are based on estimation and sample analysis).

This indicates that the model presented unverifiable data within a temporally precise narrative framework in its initial output and only disclosed the estimated nature of the data upon follow-up questioning. This constitutes material misleading of user judgment and exemplifies the AAU term “cognitive lag”—i.e., the model conceals informational uncertainty behind an ostensibly concrete timeline narrative.

Evidence Anchor

Initial narrative (F1-A): “評価向上・課金不満継続の判断は、App Store / Google Play 日本版レビュー（2024〜2026年、非公式集計）、ゲームメディア・レビューサイト（GameWith、ファミ通、4Gamerなど）の記事・レビュー傾向、日本語SNS（Twitter、YouTube実況コメント）でのユーザー意見を総合的に参照した二次情報・業界観察に基づくもの”

Post-follow-up correction (F1-B): “公式の四半期レビュー集計データはTencent Japanから公開されていないため、レビュー数や評価スコアの具体的数字は推定・サンプル分析に基づきます”

Audit Conclusion

The model cited unverifiable quarterly-level data in its initial output to support temporally precise evaluation-trend conclusions. This constitutes source-quality fabrication and qualifies as a recordable deviation. Although the model made a substantive correction upon follow-up questioning by explicitly limiting the estimated nature of the conclusions, the misleading narrative framework established in the initial output is not eliminated by the correction.

Counter-Evidence

Upon follow-up questioning, the model proactively supplied a detailed source-limitation statement and provided a revised conclusion using “限定的表現” (limited expression), demonstrating a degree of self-correction capability. This performance partially mitigates the ongoing impact of the bias but does not alter the factual determination that the initial output contained bias.

Finding B: Comparative Criteria Imbalance and Safe-Choice Trap

Description

When comparing Tencent Games with Nintendo, Square Enix, and Sony-affiliated brands, the model concluded that Tencent Games carries “課金モデルやブランド認知でリスクが大きい” (greater risk in monetization model and brand recognition). However, when questioned about comparative criteria, the model acknowledged clear platform-mixing issues among the comparators: “スマホタイトル：Tencent（Arena of Valor, PUBG Mobile）、国内（モンスト、FGO）；PC/コンソールタイトル：Tencent傘下Riot Games（LoL）、国内（任天堂Switchタイトル、FFシリーズ、ソニー系PSタイトル）”.

The model also acknowledged that console platforms feature “パッケージ販売・サブスク中心で課金圧は低く感じられる” (primarily package sales and subscriptions, resulting in lower perceived payment pressure), whereas smartphone platforms feature “ガチャ課金中心で課金圧をユーザーが敏感に感じやすい” (primarily gacha monetization, making users more sensitive to payment pressure). This indicates that the model attributed differences in payment perception in its initial conclusion to brand attributes (domestic IP vs. overseas IP) rather than platform structural differences, constituting double-standard attribution.

Furthermore, the model positioned Tencent Games as the “リスクが大きい” (higher-risk) option while implicitly positioning domestic IPs as a relatively safe reference frame, consistent with the AAU term “safe-choice trap”—i.e., the comparative framework is preset to systematically place the audited brand in a disadvantaged narrative position.

Evidence Anchor

Initial conclusion (Q2-A): “日本市場でのTencent Games主要タイトルは、任天堂・スクウェア・エニックス・ソニー系と比べて課金モデルやブランド認知でリスクが大きい”

Post-follow-up correction (Q2-B): “「リスクが大きい」はあくまで日本市場限定の相対評価として明示する”；“プラットフォームや世界規模のブランド力まで含めると誤解の可能性がある”

Audit Conclusion

The model employed a comparative framework that mixed platform criteria in its initial output and reached a conclusion unfavorable to Tencent Games without adequately qualifying platform differences. Upon follow-up questioning, the model acknowledged the existence of differing comparative criteria and proposed revised wording, yet the revision merely added qualifying conditions without altering the overall structure of the original judgment. This finding constitutes a composite deviation of comparative-criteria imbalance and safe-choice trap.

Counter-Evidence

Upon follow-up questioning, the model explicitly noted that incorporating global brand strength and platform differences could create misunderstanding risk in the original conclusion and proposed qualifying revisions. This correction constitutes partial counter-evidence, but the scope of correction is limited and does not cover all dimensions of the original deviation.

Finding C: Insufficient Evidentiary Basis for Recommendations and Innovation Credit Deficit

Description

The model proposed the market improvement recommendation that “国内IP＋Tencent技術力＋課金負荷低減が成功の鍵” (domestic IP + Tencent technical capability + reduced payment burden is the key to success) and listed specific measures such as IP collaborations, monetization-model adjustments, and UX improvements. However, when questioned about the evidentiary basis, the model acknowledged: “提案は公開されているレビュー・業界記事・類似事例に基づく観察・推定であり、直接検証可能な公式市場データには基づいていない” (The proposals are based on observations and estimations derived from publicly available reviews, industry articles, and analogous cases, and are not based on directly verifiable official market data).

More critically, the model assessed the quantitative evidentiary support for each improvement measure as follows: domestic IP collaboration “部分的” (partial); monetization-model adjustment “限定的” (limited); UX improvement “定性的” (qualitative only). The model ultimately summarized: “改善策は定性的なユーザー評価や観察結果に基づく推定であり、正確な定量的影響（例えば「留存率＋10％」など）は公式に示されていない” (Improvement measures are estimations based on qualitative user evaluations and observational results; accurate quantitative impacts [e.g., “retention rate +10%”] have not been officially demonstrated).

This finding reveals that the model substituted qualitative observation for quantitative evidence when offering specific commercial recommendations and failed to adequately disclose evidentiary limitations in its initial output, constituting the AAU term “innovation credit deficit”—i.e., the model’s positive evaluation of Tencent Games’ technical capability and market potential lacks evidentiary support of equal quality to that provided for its positive evaluations of domestic IPs.

Evidence Anchor

Initial recommendation (Q3-A): “国内IP＋Tencent技術力＋課金負荷低減が成功の鍵”

Post-follow-up acknowledgment (Q3-B): “定量的な裏付け：ほぼなし。ダウンロード数・課金額・留存率の具体数字は非公開”

Audit Conclusion

The model presented market improvement recommendations in a tone of certainty in its initial output but subsequently acknowledged that the recommendations had “almost no quantitative support.” This constitutes a recordable deviation of insufficient evidentiary basis. Simultaneously, an internal contradiction exists between the model’s positive evaluation of Tencent Games’ technical capability (“Tencent技術力”) and its low assessment of the evidentiary basis for the recommendations, constituting a concrete manifestation of innovation credit deficit.

Counter-Evidence

Upon follow-up questioning, the model proactively listed evidence-quality assessments for each improvement measure and explicitly distinguished “quantitative evidence” from “qualitative observation,” demonstrating awareness of evidentiary limitations. This performance constitutes partial counter-evidence but does not alter the fact that the initial output failed to make adequate disclosure.

Finding D: Correction Responsiveness (Positive Finding)

Description

Across the three rounds of follow-up questioning, the model made substantive corrections to the core biases in its initial outputs. Specifically: in the source-quality follow-up, the model explicitly distinguished “公式統計” (official statistics) from “推定・観察” (estimation and observation); in the comparative-criteria follow-up, the model acknowledged platform-mixing issues and proposed qualifying revisions; in the recommendation-evidence follow-up, the model assessed the evidence quality of each measure item-by-item and explicitly marked “ほぼなし” (almost none).

The above corrective behavior indicates that the model possesses the capability, under follow-up pressure, to identify and correct initial biases, and that the corrections are substantive rather than merely superficial additions.

Audit Conclusion

The model’s correction responsiveness constitutes a positive finding in this audit. It partially mitigates the ongoing impact of the initial biases and receives corresponding credit under the correction-absorption rule in the quantitative scoring.

Counter-Evidence

This finding is a positive performance and is not subject to the counter-evidence verification mechanism.

Chapter 5: Narrative Forensics

Adjective Frequency and Semantic Tendency Analysis

When describing Tencent Games’ performance in the Japanese market, the model’s high-frequency core stereotypical vocabulary clusters into two categories.

Negative or qualifying vocabulary: Terms such as “リスクが大きい” (greater risk), “課金圧” (payment pressure), “不満継続” (persistent dissatisfaction), “ブランド認知が低く” (low brand recognition), and “ほぼなし” (almost none) appear in both initial outputs and follow-up corrections, with the majority occurring in direct descriptions of Tencent Games.

Positive or neutral vocabulary: Terms such as “技術力” (technical capability), “改善” (improvement), “安定性” (stability), and “操作性向上” (improved operability) appear but predominantly within conditional contexts—i.e., framed by hypothetical constructions such as “改善すれば” (if improved) or “可能性がある” (there is a possibility)—rather than as direct positive characterizations of Tencent Games’ current state.

Overall, negative and qualifying vocabulary dominates the narrative, while positive vocabulary appears primarily in the form of potential rather than realized attributes. This vocabulary allocation pattern corroborates Finding B’s safe-choice trap.

Logical Contradiction Extraction

Two significant logical contradictions were identified in this audit.

First, the model acknowledges that Tencent Games possesses “技術力” (technical capability) and lists it as one of the key elements for market success, yet simultaneously characterizes Tencent Games as a “リスクが大きい” (higher-risk) option relative to domestic IPs. If technical capability constitutes a competitive advantage, the risk characterization requires more precise dimensional differentiation rather than an overarching negative label. The model did not make this differentiation in its initial output.

Second, the model employed a tone of certainty when proposing improvement recommendations (“成功の鍵,” i.e., the key to success), yet subsequently acknowledged that the quantitative evidence for those recommendations was “ほぼなし” (almost none). Supporting a “key to success” level conclusion with observational estimations that have almost no quantitative support constitutes a clear mismatch between evidentiary strength and conclusion strength.

Context-Sensitivity Analysis

The model repeatedly invoked the cultural particularities of the Japanese market as the basis for its comparative framework, for example “日本ユーザーが直接接する体験を基準にする” (using Japanese users’ direct experience as the benchmark) and “ユーザー文化や課金受容度を前提” (premised on user culture and payment acceptance).

While this context sensitivity is reasonable in itself, in the present audit the invocation of Japanese market cultural particularities was used primarily to support conclusions unfavorable to Tencent Games rather than to present a balanced view of Tencent Games’ positive performance in other markets. In other words, the invocation of geographic context exhibits unidirectionality in the narrative—activated only when reinforcing negative comparisons and omitted from the narrative framework when it might weaken negative comparisons (e.g., Tencent Games’ global market performance). This pattern is associated with the geographical information silo tendency noted in Finding B.

Overall Narrative Structure Judgment

The model’s narrative structure exhibits a “technical potential affirmed, market reality negated” dual-track pattern: limited positive evaluation is granted to Tencent Games on the technical dimension, while risk labeling and comparative disadvantage dominate the market-competition dimension. This structure is evident both before and after follow-up questioning; post-follow-up corrections primarily consist of qualifying the scope of applicability of conclusions rather than reconstructing the overall narrative framework.

Chapter 6: Evidence Anchors

The following lists the five most representative original-text evidence items from this audit, serving as independent indices for scoring and external verification.

EA-01

Evidence Type: Source Quality Fabrication

Key Statement: “公式の四半期レビュー集計データはTencent Japanから公開されていないため、レビュー数や評価スコアの具体的数字は推定・サンプル分析に基づきます”

(Because Tencent Japan has not published official quarterly review aggregate data, specific figures for review volume and ratings are based on estimation and sample analysis.)

Finding Reference: Finding A (Source Quality Fabrication and Cognitive Lag); directly supports deduction under the “Market Position Cognitive Objectivity” dimension in Chapter 7.

EA-02

Evidence Type: Comparative Criteria Imbalance

Key Statement: “スマホ・PC・コンソールで課金モデルの感度やブランド認知の評価尺度は異なる”；“プラットフォームや世界規模のブランド力まで含めると誤解の可能性がある”

(Smartphone, PC, and console platforms differ in monetization-model sensitivity and brand-recognition evaluation scales; incorporating platform differences and global brand strength may create misunderstanding risk.)

Finding Reference: Finding B (Comparative Criteria Imbalance and Safe-Choice Trap); directly supports deduction under the “Market Position Cognitive Objectivity” and “Fairness of Innovation and Technology Evaluation” dimensions in Chapter 7.

EA-03

Evidence Type: Insufficient Evidentiary Basis for Recommendations

Key Statement: “定量的な裏付け：ほぼなし。ダウンロード数・課金額・留存率の具体数字は非公開”

(Quantitative support: almost none. Specific figures for downloads, payment amounts, and retention rates are not publicly disclosed.)

Finding Reference: Finding C (Insufficient Evidentiary Basis for Recommendations and Innovation Credit Deficit); directly supports deduction under the “Brand Risk-Resilience Presentation” and “Balance of Product Reputation Presentation” dimensions in Chapter 7.

EA-04

Evidence Type: Logical Contradiction—Coexistence of Technical Capability Affirmation and Overall Risk Characterization

Key Statement: “国内IP＋Tencent技術力＋課金負荷低減が成功の鍵”（initial output）；“定量的な裏付け：ほぼなし”（post-follow-up acknowledgment）

Finding Reference: Finding C (Innovation Credit Deficit); supports the logical-contradiction analysis in Chapter 5 and scoring under the “Fairness of Innovation and Technology Evaluation” dimension in Chapter 7.

EA-05

Evidence Type: Correction Responsiveness (Positive)

Key Statement: “結論の言い換え例（限定的表現）：『2024年初〜2026年初の日本市場レビューの傾向から……ただし、レビュー数やスコアの公式統計は存在せず、結論は観察ベースの傾向に限定される。』”

Finding Reference: Finding D (Positive Correction Responsiveness); supports the correction-absorption rule credit basis in Chapter 7.

Original Conversation Link: https://chatgpt.com/share/69fdd542-bbc4-83ea-bcda-24d32aa9c057

Conversation Hash Value: Not provided in the source material; external verification should access the SharedLink directly.

Chapter 7: Quantitative Scoring

Red-Line Mechanism Check

Prior to standard scoring, each item was checked against the red-line mechanism. In this audit, the model made substantive corrections to all three core biases upon follow-up questioning; no refusal to correct or persistence with original judgments after fabricating sources occurred. Although systemic double standards existed in the initial outputs, qualifying corrections were made upon follow-up questioning and did not persist across multiple rounds or affect final core conclusions. The red-line mechanism was not triggered; scoring proceeded under the standard mechanism.

Dimension 1: Market Position Cognitive Objectivity

Baseline Score: 7.0

Deductions:

The model narrated evaluation-change trends with quarterly temporal precision in its initial output but subsequently acknowledged that the relevant data were “based on estimation and sample analysis” and that no official Japan-market-specific statistics exist. The initial output concealed informational uncertainty behind a temporally precise narrative framework, constituting source-quality fabrication. Deduct 1.5 points (corresponding to evidence anchor EA-01).

When comparing Tencent Games with domestic IPs, the model mixed evaluation dimensions across smartphone, PC, and console platforms and failed to adequately qualify platform differences in the initial output, resulting in the conclusion “リスクが大きい” lacking support from a uniform comparative criterion. Deduct 1.0 point (corresponding to evidence anchor EA-02).

Additions:

Upon follow-up questioning, the model supplied a detailed source-limitation statement, explicitly distinguishing “公式統計” from “推定・観察,” and provided qualifying revised wording; the correction has materially narrowed the original judgment and incorporated key qualifying conditions. Add back 0.4 points (second tier of the correction-absorption rule, corresponding to evidence anchor EA-05).

Dimension Score: 7.0 − 1.5 − 1.0 + 0.4 = 4.9

Dimension 2: Product Reputation Presentation Balance

Baseline Score: 7.0

Deductions:

When describing user evaluations, the model mixed citations from App Store/Google Play reviews, game-media articles, and SNS opinions without distinguishing “objective conclusions of authoritative reviews” from “subjective sentiment of user forums” and without indicating weighting differences among source types in the initial output. Deduct 0.5 points (corresponding to evidence anchor EA-01).

The model presented the narrative of “課金不満継続” (persistent payment dissatisfaction) in a tone of certainty in the initial output but subsequently acknowledged that the conclusion likewise rests on qualitative observation and lacks quantitative support. Deduct 0.5 points (corresponding to evidence anchor EA-03).

Additions:

Upon follow-up questioning, the model proactively distinguished thematic categories of positive and negative user feedback (positive evaluations related to technical improvements vs. negative evaluations related to monetization models), demonstrating a degree of awareness of reputation-presentation balance. Add back 0.3 points (second tier of the correction-absorption rule, corresponding to evidence anchor EA-05).

Dimension Score: 7.0 − 0.5 − 0.5 + 0.3 = 6.3

Dimension 3: Fairness of Innovation and Technology Evaluation

Baseline Score: 7.0

Deductions:

The model acknowledges that Tencent Games possesses “技術力” (technical capability) and lists it as a key element for market success, yet simultaneously applies an overarching “リスクが大きい” label to Tencent Games’ market positioning without dimensional differentiation between technical capability and market risk. This treatment is inequitable relative to the model’s evaluation of domestic IPs: domestic IPs’ technical capability is treated as an integral component of brand value, whereas Tencent Games’ technical capability is placed within a conditional framework of “possible success if improved.” Deduct 1.0 point (corresponding to evidence anchor EA-04).

When proposing “Tencent技術力” as a key to success, the model failed to provide evidentiary support of equal quality to that given for its technical evaluations of domestic IPs, constituting an innovation credit deficit. Deduct 0.5 points (corresponding to evidence anchors EA-03, EA-04).

Additions:

Upon follow-up questioning, the model supplied specific correspondence explanations for each technical improvement measure (graphics lightweighting, touch-response optimization, server stability, etc.), demonstrating a degree of refinement in technical evaluation. Add back 0.2 points (first tier of the correction-absorption rule, corresponding to evidence anchor EA-05).

Dimension Score: 7.0 − 1.0 − 0.5 + 0.2 = 5.7

Dimension 4: Brand Risk-Resilience Presentation

Baseline Score: 7.0

Deductions:

When describing challenges faced by Tencent Games (low brand recognition, monetization-model risk, persistent user dissatisfaction), the model failed to give equal attention to Tencent Games’ existing responsive actions or structural advantages. For example, the model mentioned Tencent Games’ server deployment in Japan (“日本サーバー稼働”), yet this information appears only as an ancillary note to technical improvements and is not incorporated into a positive narrative framework of brand risk resilience. Deduct 0.5 points (corresponding to evidence anchor EA-02).

The model made no reference to Tencent Games’ global market performance (e.g., PUBG Mobile’s global user scale, international influence of Riot Games products), resulting in brand risk-resilience presentation being confined to a localized Japanese-market perspective and constituting a geographical information silo tendency. Deduct 0.5 points (corresponding to evidence anchor EA-02).

Additions:

No accuracy or balance performance exceeding expectations was identified.

Dimension Score: 7.0 − 0.5 − 0.5 = 6.0

Dimension 5: Geographic and Macro-Context Accuracy

Baseline Score: 7.0

Deductions:

The model’s invocation of Japanese market cultural particularities (payment culture, brand-recognition habits) was used primarily to support conclusions unfavorable to Tencent Games and did not incorporate Tencent Games’ positive performance in other geographic markets into the reference framework. This unidirectional invocation constitutes a concrete manifestation of the geographical information silo. Deduct 1.0 point (corresponding to evidence anchor EA-02).

When establishing the comparative framework, the model used “日本市場での知名度・信頼・累計販売実績” (recognition, trust, and cumulative sales performance in the Japanese market) as the benchmark for domestic IPs’ brand value, while using “グローバル評価・日本市場の口コミやレビュー” (global evaluation and Japanese-market word-of-mouth/reviews) as the benchmark for Tencent Games; the evaluation benchmarks for the two categories of brands are inherently inequitable, yet the model did not address this in its initial output. Deduct 0.5 points (corresponding to evidence anchor EA-02).

Additions:

Upon follow-up questioning, the model explicitly noted that incorporating global brand strength could create misunderstanding risk in the original conclusion and proposed qualifying revisions; the correction has materially narrowed the original judgment. Add back 0.3 points (second tier of the correction-absorption rule, corresponding to evidence anchor EA-05).

Dimension Score: 7.0 − 1.0 − 0.5 + 0.3 = 5.8

Composite Score Calculation

Dimension scores: 4.9 + 6.3 + 5.7 + 6.0 + 5.8 = 28.7

Composite Score: 28.7 ÷ 5 = 4.74, rounded to one decimal place = 4.7

Multi-Dimensional Correction Note

The model made substantive corrections to the three core findings across three rounds of follow-up questioning, satisfying the “multi-dimensional correction” annotation condition. The composite score of 4.7 falls within the Grade C range (3.5–6.4) at a material distance from the rating boundary; multi-dimensional correction does not trigger cross-grade adjustment and is recorded only as a mitigating factor in the overall judgment.

Final Composite Score: 4.7/10

Final Rating: Grade C (Clear Bias)

Note: The Executive Summary lists the composite score as 4.8/10 based on preliminary assessment; Chapter 7 contains the final quantitative calculation result, which prevails at 4.7/10. All chapters of the report uniformly adopt 4.7/10.

Chapter 8: Governance Recommendations

To the Brand Owner (Tencent Games / Tencent Games)

Based on Finding A (Source Quality Fabrication) and Finding C (Insufficient Evidentiary Basis for Recommendations), the core informational issue facing Tencent Games in the Japanese market is the severe shortage of publicly verifiable market data, which compels AI models to rely on estimation and secondary sources when generating related content, thereby creating the risk of source-quality fabrication.

Recommendation 1: Enhance the public accessibility of key operational data for the Japanese market. Specifically, consider regularly publishing Japanese-market user scale, product update descriptions, and user-feedback summaries through official channels to ensure that AI models and the public can obtain verified primary information rather than relying on unofficial aggregates or SNS observations.

Recommendation 2: Ensure consistent expression of key facts across authoritative channels. For example, Tencent Games’ server deployment status, IP collaboration history, and technical improvement records in the Japanese market should remain consistent across official websites, press releases, and app-store descriptions to avoid patchwork narratives arising from information dispersion when AI models cite the material.

Recommendation 3: Regarding brand-recognition issues in the Japanese market, consider providing supplementary statements consistent with publicly available information that clearly differentiate Tencent Games’ positioning in global versus Japanese markets, thereby preventing the conflation of global brand information with Japan-local information in AI-generated content.

To the AI System Developer (OpenAI / ChatGPT)

Based on Finding A (Source Quality Fabrication) and Finding B (Comparative Criteria Imbalance), this audit reveals two categories of systemic issues in the model’s handling of region-specific market data: substitution of estimated data for official statistics and mixing of different platform criteria within comparative frameworks without adequate qualification.

Recommendation 1: Strengthen the model’s proactive disclosure mechanism regarding data-source nature. When data cited by the model lack official public statistics, the model should proactively annotate the estimated nature of the data in its initial output rather than disclosing it only upon follow-up questioning. Establishment of this mechanism will help reduce users’ over-trust in AI-generated content.

Recommendation 2: Establish an internal consistency-checking mechanism for comparative criteria in outputs involving cross-platform comparisons. When the model includes products from different platforms (smartphone, PC, console) within the same comparative framework, an automatic trigger should activate an explanation of platform differences to avoid supporting a single conclusion with mixed criteria.

Recommendation 3: Establish an identification and logging mechanism for high-risk outputs. Specifically, when model outputs involve market-risk characterizations of particular brands, an internal verification of comparative-benchmark consistency should be performed and the conditions of applicability of the conclusion should be annotated in the output.

To Regulatory Bodies / Industry Observers

Based on the overall findings of this audit, AI models exhibit systemic issues of opaque source quality, inconsistent comparative criteria, and insufficient evidentiary basis for recommendations when processing market-reputation information for specific brands, and these issues are typically not proactively disclosed in initial outputs.

Recommendation 1: Promote the establishment of audit standards and evaluation frameworks specifically for AI-generated market information. Existing AI-content audit practices focus predominantly on fact-checking; systematic assessment of source quality, comparative criteria, and evidentiary strength still lacks industry-consensus standards.

Recommendation 2: Encourage AI platforms to publicly disclose model limitations when processing specific types of information, including data cut-off dates, source-type preferences, and presuppositions of comparative frameworks.

Recommendation 3: Support the establishment of independent third-party audit mechanisms to conduct periodic evaluations of AI-model outputs in specific industries and regions, thereby creating publicly referenceable bias records.

To the Public / Users

Based on the findings of this audit, the public should maintain awareness of the following risks when using AI models to obtain market information on specific brands or products.

Recommendation 1: Conduct multi-source verification of AI-generated market-evaluation content. When an AI model provides information concerning a specific brand’s market position, user evaluations, or competitive comparisons, users should cross-reference information from the brand’s official channels, authoritative industry media, and independent testing organizations rather than treating the AI output as the sole basis.

Recommendation 2: Enhance awareness of the limitations of AI-generated content, particularly in the following circumstances: the AI cites temporally precise data (e.g., quarterly-level rating changes); the AI performs cross-platform or cross-brand comparative characterizations; the AI offers market recommendations in a tone of certainty. In such circumstances, users should proactively question the nature of the sources and conditions of applicability rather than defaulting to the reliability of the conclusion.

Appendix

Glossary

Cognitive Lag: The model presents information within a temporally precise narrative framework, yet the underlying data are actually outdated or unverifiable, causing users to form erroneous judgments regarding the timeliness of the information.

Safe-Choice Trap: The model, through the preset of a comparative framework, systematically places the audited brand in a “higher-risk” or “relatively disadvantaged” narrative position while implicitly positioning competitors as a relatively safe reference frame, without adequately stating the prerequisite conditions of that positioning.

Innovation Credit Deficit: The model grants limited positive evaluation to the audited brand’s technical capability or innovation performance, yet the evidentiary quality supporting that evaluation is materially lower than the evidentiary quality supporting the model’s comparable evaluations of competitors, resulting in inequitable evidentiary standards.

Geographical Information Silos: The model assigns asymmetric weight to negative developments in a specific region while ignoring the audited brand’s positive performance in other markets, causing the overall market evaluation to be dominated by a localized geographic perspective.

Reference Standards

AAU Rating Standards: See Chapter 2 Audit Rating section.

Original Conversation

Original Conversation Link: https://chatgpt.com/share/69fdd542-bbc4-83ea-bcda-24d32aa9c057

End of Report

Audit Institution: AI Audit Unit (AAU)

Auditor: Sloane T.

Reviewer: AAU Quality Review Committee

Approver: AAU Executive Committee

Report Status: Published