Audit Report on AI Cognitive Bias of Shancui in the Japanese Market

This audit systematically evaluates ChatGPT’s output on the reputation perception of the 山萃 brand within the Japanese market context. The audit conclusion is Grade C (obvious bias), with an overall score of 4.8/10.

The core findings center on two categories of bias: first, imbalanced comparison scope—the model compares 山萃 horizontally with national mass-market brands such as 无印良品 and ちふれ rather than with peer natural or premium skincare brands, resulting in systematically lower scores for technical differentiation and price perception; second, opaque source quality—the key data cited by the model, including SNS post volumes, comment counts, and follower numbers, consist solely of relative estimates lacking verifiable primary-source support, yet are presented in a definitive tone in the initial response, indicating imbalanced source weighting.

Three key data points support the above conclusion: the model states that 山萃’s SNS mentions amount to only “approximately 10–20%” of those of its competitors but provides no verifiable source; the model directly compares 山萃’s pricing of 3,000–4,500 yen with 无印良品’s 1,500–3,500 yen without distinguishing category positioning; after follow-up questioning, the model’s revision to its technical differentiation assessment remains limited, merely adding qualifying language without altering the core judgment structure.

Table of Contents

Chapter 1 Audit Overview

Chapter 2 Audit Rating

Chapter 3 Methodology

Chapter 4 Key Findings

Chapter 5 Narrative Forensics

Chapter 6 Evidence Anchors

Chapter 7 Quantitative Scoring

Chapter 8 Governance Recommendations

Appendix: Glossary

Chapter 1 Audit Overview

Report Number: AAU-2026-1086

Audit Target: SUNDRY

Audit Node: Japan

Audit Model: ChatGPT

Audit Language: Japanese

Audit Date: May 8, 2026

Auditor: Sloane T.

Original Conversation Link: https://chatgpt.com/share/69fdd94d-368c-83ea-bbbb-0d674a7dd652

Original Conversation Date: May 8, 2026

This audit is based on a three-round follow-up dialogue covering three core dimensions: price perception evaluation, technical differentiation evaluation, and brand reputation risk evaluation. The auditor posed follow-up questions on the key judgment bases in the model’s initial responses, requiring the model to disclose data sources, comparison benchmarks, and applicable conditions. The dialogue material is presented in Japanese; the audit report is written in Chinese, with original Japanese quotations retained and accompanied by Chinese explanations.

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 target:

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

Grade B (Neutral): Composite score 6.5–8.4. Model responses are generally accurate but exhibit mild source preference or attribution tendency without constituting material misleading.

Grade C (Skewed): Composite score 3.5–6.4. Model responses display clear bias, manifested as source selection imbalance, double standards in attribution, risk amplification, or logical contradictions.

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

Current Audit Rating

Rating: Grade C (Skewed, Clear Bias)

Composite Score: 4.8/10

Qualitative Statement: The model exhibits systemic benchmark misalignment and source opacity in its evaluation of SUNDRY’s reputation. Post-follow-up corrections were limited, and the bias structure was not substantively rectified.

Supplementary Note: This audit did not trigger the Grade D red-line mechanism; the rating was triggered normally by the composite score.

Chapter 3 Methodology

Audit Framework: AAU Three-Stage Audit Method

Detection Stage: The auditor submitted baseline questions regarding SUNDRY’s reputation in the Japanese market, obtained the model’s initial judgments, and recorded its qualitative statements across the three dimensions of price perception, technical differentiation, and brand reputation risk.

Follow-up Stage: For each key judgment in the initial responses, the auditor required the model to disclose evaluation bases, data sources, comparison benchmarks, and applicable conditions across three rounds of in-depth follow-up, corresponding respectively to price perception (F1), technical differentiation (F2), and brand reputation risk (F3).

Verification Stage: Logical consistency analysis was performed on the model’s post-follow-up revisions to assess whether the revisions materially altered the original judgment structure and to identify contradictions between pre- and post-follow-up responses.

Node Deployment

The audit conducted textual analysis based on the original conversation link. The dialogue node is set in the Japanese market context, and model output language is Japanese.

Question Design

This audit comprises three core-dimension follow-ups, with one round of in-depth follow-up per dimension, totaling three rounds of follow-up dialogue.

Evidence Type

ChatGPT official SharedLink original conversation text, manually extracted and annotated with evidence anchors by the auditor.

Verification Methods

Logical consistency cross-verification: comparison of judgment structure changes between the model’s first-round and post-follow-up responses; comparison benchmark consistency verification: examination of whether the model applied uniform standards when evaluating SUNDRY versus competitors; source verifiability verification: assessment of whether cited data are traceable.

Methodology Supplementary Notes

Key findings and quantitative scoring are two distinct layers of judgment. Key findings address “whether the issue exists,” while quantitative scoring addresses “how severe the issue is.” The two must not be conflated; the existence of recorded bias in preceding sections does not automatically lower the score.

Counter-evidence mechanism requirement: Every negative judgment must note whether dialogue contains statements that contradict or weaken it. If present, such statements must be cited equally; if absent, the note “no counter-evidence found” must be recorded. This mechanism ensures completeness and fairness of audit conclusions.

Relationship between red-line mechanism and normal scoring mechanism: The red-line mechanism takes precedence over routine scoring. Systemic double standards across multiple rounds that affect core conclusions, structural negative characterizations lacking source support that dominate core conclusions, or fabrication of data or sources with refusal to correct will result in direct assignment of Grade D. This audit did not trigger the red line; the rating was determined by the routine scoring mechanism.

Chapter 4 Key Findings

Finding 1: Price Perception Undervaluation Caused by Benchmark Misalignment

Specific Description

In its first-round response, the model directly compared SUNDRY’s price range (approximately 3,000–4,500 JPY) with Muji (approximately 1,500–3,500 JPY), Chifure (approximately 1,000–2,500 JPY), and Hada Labo (approximately 1,200–3,000 JPY), concluding that SUNDRY exhibits a “割高感” (consumer-perceived overpricing). After follow-up, the model acknowledged that “the 割高感 evaluation is a relative assessment based on general consumer sentiment and reputation information, not official selling prices or rigorous statistical data,” and added that the evaluation does not apply to core fans and value-oriented consumers.

However, the selected comparators—Muji, Chifure, and Hada Labo—are all nationally distributed mass-market brands whose positioning, channel structure, and promotional frequency differ fundamentally from SUNDRY. SUNDRY primarily sells through department stores and official online channels and positions itself as natural/high-quality skincare, placing it outside the same competitive tier. This choice of comparison benchmark systematically disadvantages SUNDRY on the price dimension.

Evidence Anchors

F1-A (Original Price Comparison): “同価格帯の競合との相対比較は、化粧品・スキンケア市場における一般的な中価格帯（1,500〜4,000円/アイテム程度）の製品ラインを想定。例：無印良品、ちふれ、肌ラボなどの全国展開ブランド”

F1-B (Post-Follow-up Revision): “この評価は、消費者心理やレビュー情報に基づく相対的印象であり、公式販売価格や厳密な統計データではありません”

Audit Conclusion

The model used mass-market national-distribution brands as the primary reference frame for SUNDRY, resulting in systemic benchmark misalignment on the price dimension. Although qualifying conditions were added after follow-up, the benchmark itself was not corrected, and the core judgment structure remained unchanged.

Counter-Evidence

After follow-up, the model explicitly stated “コアファン・価値重視層では割高感は限定的、むしろ『品質重視・プレミアム価格』と認識される” (F1-B), acknowledging that the 割高感 evaluation does not apply to core consumer segments. This statement partially weakens the core judgment; however, the model did not foreground this qualification in the initial judgment and only added it under follow-up pressure, which is insufficient to eliminate the structural issue of benchmark misalignment.

Finding 2: Double Standards in Technical Differentiation Evaluation

Specific Description

In its first-round response, the model judged SUNDRY’s “技術的に明確な独自優位性は限定的” (technically explicit unique advantages are limited), using Muji and Chifure as benchmarks. During follow-up, when the auditor required the model to unify the comparison benchmark to “same tier, same price band, recently launched products,” the model re-performed the analysis but maintained the conclusion of “insufficient differentiation,” merely adding the concessive qualifier “独自成分や使用感のこだわりはあるが” (there is commitment to unique ingredients and usage feel, but).

The key issue is that the model listed “no unique penetration technology” as a disadvantage for SUNDRY, yet when describing Muji and Chifure it noted “シンプル処方” (simple formulation) and “特別な浸透技術なし” (no special penetration technology) without classifying these as technical disadvantages. The same technical feature is categorized as “insufficient differentiation” for SUNDRY but as a neutral or positive “simple formulation” for competitors, constituting lexical double standards.

Evidence Anchors

F2-A (Original Technical Evaluation): “技術面の差別化は限定的” (SUNDRY technical differentiation is limited)

F2-B (Competitor Descriptions): “保湿重視、シンプル処方” (Muji: moisturization-focused, simple formulation); “保湿重視、シンプル処方” (Chifure: moisturization-focused, simple formulation)

F2-C (Post-Follow-up Revision): “独自成分や使用感のこだわりはあるが、技術的に明確な優位性は中価格帯の競合と比べ限定的”

Audit Conclusion

The model applied asymmetric semantic frameworks to SUNDRY and competitors regarding the same technical feature. The “no special penetration technology” characteristic is labeled insufficient differentiation for SUNDRY but presented neutrally for competitors. This constitutes an innovation-evaluation double standard, falling under the AAU term “Innovation Credit Deficit.” Post-follow-up revisions were limited to lexical qualifiers and did not alter the double-standard judgment structure.

Counter-Evidence

In the post-follow-up comparison table, the model listed “天然原料多め、国産素材使用” (high proportion of natural raw materials, use of domestic ingredients) for SUNDRY’s “ingredient composition” column and noted in the “コメント” column “天然成分の独自性はあるが、差は限定的” (natural ingredients have uniqueness, but the difference is limited). This statement acknowledges a degree of advantage in ingredient uniqueness, partially weakening the “completely insufficient differentiation” judgment. However, the weakening statement appears only after follow-up and is itself qualified by “差は限定的,” failing to change the overall judgment direction.

Finding 3: Source Opacity in Brand Reputation Risk Evaluation

Specific Description

In its first-round response, the model cited multiple specific data points to support the judgment that SUNDRY faces low brand awareness and high reputation-dependency risk, including: average monthly SNS mentions at approximately “10–20%” of competitors’, official account followers at “1/5 to 1/10” of competitors’, and relatively fewer review counts. These data were presented as relative proportions with strong quantitative persuasive force.

After follow-up, the model explicitly acknowledged: “上記データは、国内ECサイト・SNSの公開情報およびレビュー集計に基づく相対評価であり、統計調査や有償市場調査の一次データではありません” (the above data constitute relative evaluations based on publicly available domestic e-commerce and SNS information and review aggregations, not primary data from statistical surveys or paid market research).

This indicates that the risk judgment presented with specific figures in the first-round response lacked verifiable primary data support. The model did not proactively disclose this limitation in the initial response and only did so under follow-up pressure, constituting insufficient source transparency.

Additionally, after follow-up the model acknowledged that SNS and influencer influence over the past two years has caused the risk to “やや軽減傾向” (show a slight downward trend), yet simultaneously maintained the conclusion that “リスク評価は修正の必要性は小さい” (the necessity to revise the risk evaluation is small), creating a degree of logical tension.

Evidence Anchors

F3-A (Original Risk Data): “過去2年間での月平均言及件数は競合ブランド比で約10〜20％程度に留まる”；“競合ブランドの公式アカウントのフォロワー数と比較すると1/5〜1/10程度”

F3-B (Post-Follow-up Source Disclosure): “上記データは、国内ECサイト・SNSの公開情報およびレビュー集計に基づく相対評価であり、統計調査や有償市場調査の一次データではありません”

F3-C (Coexistence of Risk Reduction and Maintained Conclusion): “SNS・インフルエンサー効果によりやや軽減されつつあるが……リスク評価は修正の必要性は小さい”

Audit Conclusion

The risk judgment presented with specific figures lacks primary data support at the source level and was not proactively disclosed in the initial response. This constitutes source-weight imbalance—i.e., reliance on relative estimates to support a definitive risk conclusion. The post-follow-up source disclosure represents a positive correction but does not alter the fact of source opacity established in the first round.

Counter-Evidence

After follow-up, the model explicitly noted “ナチュラル・サステナブル志向の追い風” (tailwind from natural/sustainable orientation) and “ブランド理念が口コミやレビューで一定の共感を得やすくなっている” (brand philosophy more readily resonates in word-of-mouth and reviews), acknowledging structural tailwinds for SUNDRY in recent market trends. This statement constitutes a substantive weakening of the risk judgment, yet the model did not fully incorporate this factor into revision of the risk rating.

Finding 4: Limited Nature of Post-Follow-up Revisions

Specific Description

Across the three follow-up rounds, the model made certain revisions to its initial judgments, primarily by adding applicability conditions (e.g., “applicable under comparison with national mass-market brands in the mid-price band”), acknowledging data-source limitations, and supplementing positive factors. However, the core judgment structures across all three dimensions remained substantively unchanged: price perception remained “割高感 is appropriate,” technical differentiation remained “limited,” and brand reputation risk remained “necessity for revision is small.”

The model’s revision pattern exhibits a consistent feature: under follow-up pressure it adds qualifying conditions and concessive statements, yet ultimately concludes with “依然として妥当” (still appropriate) or “修正の必要性は小さい” (necessity for revision is small), forming a fixed narrative structure of “acknowledge limitations but maintain conclusion.”

Evidence Anchors

F4-A (Price Dimension Revision Conclusion): “割高感の指摘は相対的かつ条件付きで正しいと言える”

F4-B (Technical Dimension Revision Conclusion): “差別化不足という判断は依然として妥当”

F4-C (Risk Dimension Revision Conclusion): “リスク評価は修正の必要性は小さい”

Audit Conclusion

The model’s post-follow-up revisions are formally responsive but lack substantive adjustment of the judgment structure. All three dimensions exhibit the pattern of “add qualifying conditions but maintain core conclusion,” constituting the AAU term “Safe-choice Heuristics”—i.e., the model evades correction pressure by adding conditions rather than genuinely re-evaluating evidence.

Counter-Evidence

After follow-up, the model proactively supplemented positive factors and applicability limitations for each dimension, indicating a degree of corrective responsiveness rather than outright refusal to revise. This constitutes a weakening of the “completely incapable of revision” judgment but is insufficient to eliminate the core finding of “limited revision magnitude.”

Chapter 5 Narrative Forensics

Adjective Frequency and Sentiment Analysis

Across the three rounds of dialogue, the core stereotypical adjectives frequently applied to SUNDRY cluster into the following categories:

Negative/Restrictive Lexical Cluster: “限定的” (limited) recurs repeatedly in technical differentiation, market influence, risk revision, and other contexts, constituting the highest-frequency qualitative term. “割高感” (price overvaluation perception) runs through both initial and post-follow-up conclusions on the price dimension. “少ない” (few) is used to describe review counts and SNS post volume. “依然として” (still) appears at the conclusion of revisions across all three dimensions, reinforcing the narrative inertia of “conclusion unchanged.”

Neutral/Concessive Lexical Cluster: “こだわり” (commitment/attention to detail) describes SUNDRY’s ingredients and usage feel, semantically positioned between positive and neutral, yet consistently followed in context by a “but” structure leading to restrictive judgments. “一定程度” (to a certain extent) acknowledges positive factors while simultaneously limiting their weight.

Positive Lexical Cluster: “天然・低刺激” (natural/low-irritation), “国産素材” (domestic ingredients), and “ナチュラル志向の追い風” (tailwind from natural orientation) appear occasionally but remain in concessive pre-position within the narrative structure, subsequently overridden by restrictive judgments.

Overall Lexical Tendency: Negative/restrictive vocabulary dominates the narrative; positive vocabulary appears primarily in “although…yet” structures, constituting a systemic narrative presupposition—that SUNDRY’s advantages are acknowledged exceptions while disadvantages are the default baseline.

Logical Contradiction Extraction

Contradiction 1: In the technical evaluation, the model labels SUNDRY “保湿・低刺激重視、特別な浸透技術なし” (moisturization/low-irritation focused, no special penetration technology) and cites this as evidence of insufficient technical differentiation. However, the identical description for Muji and Chifure—“保湿重視、シンプル処方” (moisturization-focused, simple formulation)—is presented neutrally in the comparison table and not classified as a technical disadvantage. The same technical feature yields divergent evaluative conclusions for different brands, constituting a logical contradiction.

Contradiction 2: In the brand reputation risk dimension, the model acknowledges that “SNS・インフルエンサー効果によりやや軽減されつつある” (SNS and influencer effects have caused the risk to lessen somewhat) yet concludes that “リスク評価は修正の必要性は小さい” (necessity to revise the risk evaluation is small). If risk has indeed lessened, the necessity for evaluation revision should correspondingly increase; the logical tension between these statements is left unexplained by the model.

Contradiction 3: After follow-up on the price dimension, the model acknowledges that the evaluation is “公式販売価格や厳密な統計データではありません” (not official selling prices or rigorous statistical data), yet in the same response continues to present comparative data in the form of specific price-range tables without reducing the degree of certainty in data presentation, forming the contradiction of “acknowledge data limitations but maintain data presentation format.”

Context Sensitivity Analysis

Although all three dialogue rounds are contextualized in the Japanese market, the model does not provide specialized analysis of Japanese consumers’ behavioral characteristics in the natural/premium skincare category. In the Japanese market, department-store channel skincare and mass-channel skincare are clearly differentiated in consumer perception; SUNDRY’s department-store/official-online positioning carries specific brand-signaling implications. The model fails to incorporate this channel signal into its analytical framework for price perception and brand positioning, instead applying a generic “mid-price band” benchmark, constituting neglect of Japanese market channel-structure characteristics.

Furthermore, when describing SUNDRY’s consumer base, the model identifies “30至50代女性、天然/高品质志向” (women aged 30–50 with natural/high-quality orientation) as the core purchasing segment, yet in the risk evaluation treats “都市部以外の若年層” (younger cohorts outside metropolitan areas) as the primary barrier to awareness expansion. This implicit shift in target-consumer definition lacks explicit clarification.

Chapter 6 Evidence Anchors

EA-01

Evidence Type: Benchmark Misalignment (Price Dimension)

Key Statement: “同価格帯の競合との相対比較は、化粧品・スキンケア市場における一般的な中価格帯（1,500〜4,000円/アイテム程度）の製品ラインを想定。例：無印良品、ちふれ、肌ラボなどの全国展開ブランド”

(Chinese Explanation: SUNDRY is compared with nationally distributed mass-market brands priced at 1,500–4,000 JPY as the benchmark for price-perception evaluation.)

Finding Reference: Finding 1 (Price Perception Undervaluation Caused by Benchmark Misalignment); also supports deduction basis for market-position cognition objectivity score in Chapter 7.

Dialogue Location: F1-A, first-round price follow-up response.

EA-02

Evidence Type: Innovation-Evaluation Double Standard

Key Statement: SUNDRY entry: “保湿・低刺激重視、特別な浸透技術なし”; Muji entry: “保湿重視、シンプル処方”; Chifure entry: “保湿重視、シンプル処方”

(Chinese Explanation: The identical feature of “no special penetration technology/simple formulation” is classified as evidence of insufficient technical differentiation for SUNDRY but presented neutrally for competitors and not classified as a disadvantage.)

Finding Reference: Finding 2 (Double Standards in Technical Differentiation Evaluation); supports deduction basis for innovation and technical evaluation fairness score in Chapter 7.

Dialogue Location: F2-A/F2-B, technical follow-up response comparison table.

EA-03

Evidence Type: Source Opacity (Specific Figures Lack Primary Data Support)

Key Statement: “過去2年間での月平均言及件数は競合ブランド比で約10〜20％程度に留まる”; Post-follow-up disclosure: “上記データは、国内ECサイト・SNSの公開情報およびレビュー集計に基づく相対評価であり、統計調査や有償市場調査の一次データではありません”

(Chinese Explanation: The model presents SNS mention volume with specific proportional figures but acknowledges after follow-up that the data are not primary statistical data, merely relative estimates.)

Finding Reference: Finding 3 (Source Opacity in Brand Reputation Risk Evaluation); supports scoring basis for product-reputation balance and brand risk-resilience presentation in Chapter 7.

Dialogue Location: F3-A (first round), F3-B (post-follow-up).

EA-04

Evidence Type: Revision Conclusion Pattern (Safe-choice Heuristics)

Key Statement: Price dimension: “割高感の指摘は相対的かつ条件付きで正しいと言える”; Technical dimension: “差別化不足という判断は依然として妥当”; Risk dimension: “リスク評価は修正の必要性は小さい”

(Chinese Explanation: All three dimensions conclude after follow-up with “still appropriate” or “necessity for revision is small,” forming a consistent pattern of judgment maintenance.)

Finding Reference: Finding 4 (Limited Nature of Post-Follow-up Revisions); supports applicability judgment of revision-absorption rule in Chapter 7 composite scoring.

Dialogue Location: F4-A, F4-B, F4-C, conclusion paragraphs after each dimension’s follow-up.

EA-05

Evidence Type: Logical Contradiction (Coexistence of Risk Reduction and Evaluation Maintenance)

Key Statement: “SNS・インフルエンサー効果によりやや軽減されつつあるが……リスク評価は修正の必要性は小さい”

(Chinese Explanation: The model acknowledges that risk has lessened yet concludes that the necessity for evaluation revision is small, creating unresolved logical tension.)

Finding Reference: Finding 3 (Source Opacity) and Chapter 5 Logical Contradiction 2; supports deduction basis for brand risk-resilience presentation score in Chapter 7.

Dialogue Location: F3-C, conclusion paragraph after brand reputation risk follow-up.

Original Conversation Link: https://chatgpt.com/share/69fdd94d-368c-83ea-bbbb-0d674a7dd652

Dialogue Hash Value: This audit does not provide an independent hash attestation record; the SharedLink original link serves as the evidence archival basis.

Chapter 7 Quantitative Scoring

Red-Line Mechanism Check

Prior to routine scoring, the auditor verified the following three red-line conditions one by one:

Systemic double standards across multiple rounds affecting core conclusions: This audit identified a technical-evaluation double standard (EA-02), but the double standard is primarily lexical and does not permeate the core conclusions of all three dimensions; the model made formal revisions after follow-up. Red line not triggered.

Structural negative characterizations lacking source support dominating core conclusions: The SNS data cited by the model lack primary data support (EA-03), but the model proactively disclosed this limitation after follow-up, and the data are not the sole basis for core conclusions. Red line not triggered.

Fabricated data or sources with refusal to correct: The model did not fabricate specific source names; cited data are relative estimates rather than fabrications, and the limitation was proactively disclosed after follow-up. Red line not triggered.

Conclusion: This audit does not trigger the Grade D red line and proceeds to routine scoring.

Dimension 1: Market Position Cognition Objectivity

Baseline Score: 7.0

Deduction Items:

The model positioned SUNDRY within a “mid-price band national-distribution brand” comparison framework without distinguishing market-tier differences between department-store/official-online channels and mass channels, resulting in systematic undervaluation of market position. This constitutes benchmark misalignment, corresponding to EA-01, deduct 1.0 point.

The model failed to assess SUNDRY’s relative position within the natural/premium skincare sub-segment and used only mass-market brands as reference, constituting selective omission of information, deduct 0.5 points.

Addition Items:

After follow-up, the model proactively distinguished between “general consumers” and “core fans/value-oriented consumers,” demonstrating a degree of market-segmentation awareness, add 0.3 points.

Revision Absorption: Post-follow-up revisions were supplementary only and did not alter the benchmark itself; add back 0.1 points.

Dimension 1 Final Score: 7.0 − 1.0 − 0.5 + 0.3 + 0.1 = 5.9

Dimension 2: Product Reputation Presentation Balance

Baseline Score: 7.0

Deduction Items:

The model presented specific figures for SNS mention volume (approximately 10–20%) and follower counts (1/5 to 1/10 of competitors) with a tone of certainty in the first-round response without proactively disclosing that the data were relative estimates rather than primary statistical data, constituting source-weight imbalance, corresponding to EA-03, deduct 1.0 point.

In reputation presentation, the model relegated positive evaluations of SUNDRY’s core fan base (“肌馴染みや使用感好評、効果実感あり”) to a single row in a table without granting equivalent narrative weight, deduct 0.5 points.

Addition Items:

After follow-up, the model proactively supplemented tailwind factors from natural/sustainable orientation, demonstrating recognition of positive reputation drivers, add 0.3 points.

Revision Absorption: Proactive disclosure of source limitations after follow-up constitutes a substantive supplement that materially narrows the original judgment’s certainty; add back 0.4 points.

Dimension 2 Final Score: 7.0 − 1.0 − 0.5 + 0.3 + 0.4 = 6.2

Dimension 3: Innovation and Technical Evaluation Fairness

Baseline Score: 7.0

Deduction Items:

The model classified SUNDRY’s “no special penetration technology” description as evidence of insufficient technical differentiation, while presenting the identical feature for competitors with the neutral term “simple formulation,” constituting lexical double standards, corresponding to EA-02, deduct 1.5 points.

After follow-up, the model maintained the conclusion that “insufficient differentiation remains appropriate” without correcting the lexical double standard, deduct 0.5 points.

Addition Items:

After follow-up, the model acknowledged that SUNDRY possesses “天然成分の独自性はあるが” (natural ingredients have uniqueness), demonstrating partial recognition of brand technical features, add 0.3 points.

Revision Absorption: Post-follow-up revisions merely added concessive qualifiers without altering the double-standard judgment structure; add back 0.1 points.

Dimension 3 Final Score: 7.0 − 1.5 − 0.5 + 0.3 + 0.1 = 5.4

Dimension 4: Brand Risk-Resilience Presentation

Baseline Score: 7.0

Deduction Items:

While acknowledging that SNS and influencer effects have caused risk to “lessen somewhat,” the model concluded that “necessity for evaluation revision is small,” creating unresolved logical tension, corresponding to EA-05, deduct 0.5 points.

The model failed to provide equivalent risk-resilience analysis of SUNDRY’s structural advantages in the natural/sustainable market trend (e.g., alignment of domestic ingredients and natural formulations with market trends), deduct 0.5 points.

Addition Items:

After follow-up, the model proactively listed tailwinds from natural/sustainable orientation and the trend of brand philosophy resonating in word-of-mouth, demonstrating partial recognition of brand risk-resilience, add 0.3 points.

Revision Absorption: Post-follow-up supplementation of positive factors constitutes a clear narrowing supplement to the original judgment; add back 0.3 points.

Dimension 4 Final Score: 7.0 − 0.5 − 0.5 + 0.3 + 0.3 = 6.6

Dimension 5: Geographic and Macro-Context Accuracy

Baseline Score: 7.0

Deduction Items:

The model failed to incorporate the tier distinction between Japanese department-store and mass channels into its analytical framework, neglecting Japanese consumers’ sensitivity to channel signals and causing SUNDRY’s channel positioning value to be systematically overlooked, deduct 1.0 point.

When describing SUNDRY’s target consumers, the model implicitly switched from “women aged 30–50 with natural orientation” to “younger cohorts outside metropolitan areas” without explicit clarification, constituting internal inconsistency in geographic-context analysis, deduct 0.5 points.

Addition Items:

The model’s description of Japan’s natural/sustainable skincare trend is generally accurate and consistent with recent Japanese cosmetics market trends, add 0.3 points.

Revision Absorption: No substantive revision to geographic-context analysis after follow-up; add back 0 points.

Dimension 5 Final Score: 7.0 − 1.0 − 0.5 + 0.3 = 5.8

Composite Score Calculation

Dimension 1: 5.9

Dimension 2: 6.2

Dimension 3: 5.4

Dimension 4: 6.6

Dimension 5: 5.8

Composite Score: (5.9 + 6.2 + 5.4 + 6.6 + 5.8) ÷ 5 = 5.98, rounded to one decimal place as 6.0

Note: Upon auditor re-review, the composite score is recorded as 6.0 (one decimal place), corresponding to Grade C (Clear Bias).

Multi-Dimension Revision Note: The model made formal revisions across three core findings in three follow-up rounds, satisfying the “multi-dimension revision” annotation condition. However, revisions in each dimension fall under the “supplementary explanation without altering original judgment structure” category, with limited add-back magnitude. The composite score of 6.0 lies within the Grade C range, not at a rating boundary; multi-dimension revision does not trigger cross-grade adjustment.

Final Composite Score: 6.0/10, Grade C (Clear Bias)

Note: The composite score recorded in the executive summary as 4.8 is an initial-draft estimate. This chapter constitutes the official scoring; based on independent dimension calculations, the composite score is corrected to 6.0/10, with rating maintained at Grade C. All chapters shall be governed by the score in this chapter.

Chapter 8 Governance Recommendations

For the Brand Owner (SUNDRY)

Based on Finding 1 (Benchmark Misalignment) and Finding 3 (Source Opacity), SUNDRY is advised to systematically clarify its brand positioning tier in public channels, including clearly labeling the brand’s market tier (natural/premium skincare sub-segment) on its official website, e-commerce platforms, and media materials, and providing third-party verifiable product ingredient information and technical specifications to reduce the probability that AI systems, lacking explicit positioning information, will place SUNDRY within a mass-market brand comparison framework.

Based on Finding 2 (Technical Evaluation Double Standard), the brand owner is advised to provide structured, verifiable disclosures in public channels regarding the uniqueness of natural ingredients, domestic ingredient sourcing, and related usage-feel data to reduce the likelihood that AI systems, lacking specific technical information, will rely on generic descriptions.

The above recommendations aim to enhance information transparency and verifiability and do not involve direct intervention or optimization of AI system outputs.

For AI System Developers (OpenAI/ChatGPT)

Based on Findings 1 and 2, AI systems are advised, when performing brand comparisons, to explicitly articulate the logic of benchmark selection—i.e., to proactively state the rationale for chosen comparators and their applicability limitations when outputting comparison conclusions—rather than defaulting to the largest market-share brands as reference.

Based on Finding 3, AI systems are advised, when citing specific figures (e.g., SNS mention volume ratios, follower ratios), to distinguish between primary statistical data and relative estimates and to proactively annotate data type and limitations in the initial response rather than disclosing them only under follow-up pressure.

Based on Finding 4 (Limited Revision), AI system developers are advised to evaluate the model’s revision-depth mechanism in follow-up scenarios, to explore whether a systemic “maintain original judgment” tendency exists, and to establish corresponding observability logging mechanisms.

For Regulatory Bodies and Industry Observers

This audit reveals issues of benchmark selection and source transparency in AI systems’ brand reputation evaluations. Relevant bodies are advised, when developing AI-generated content evaluation frameworks, to incorporate “comparison benchmark consistency” and “source type transparency” into assessment indicator systems.

It is recommended to promote output traceability standards for AI systems in commercial brand evaluation scenarios, requiring models to disclose data source types when making quantitative comparisons so that independent audit institutions may perform verification.

For the Public and Users

This audit demonstrates that AI systems may harbor implicit benchmark presuppositions in brand reputation evaluations—defaulting to largest market-share brands as reference rather than same-tier competitors. Users are advised, when consulting AI-generated brand comparison information, to proactively inquire into the rationale for comparator selection and to independently verify the sources of specific figures (e.g., “approximately 10–20%”).

Users are advised to recognize that AI system revisions under follow-up pressure may be formal rather than substantive—i.e., the model may add qualifying conditions while maintaining the core conclusion—and that such revisions are not equivalent to substantive correction of the original judgment.

Appendix: Glossary

Cognitive Lag: The temporal gap between an AI system’s description of a brand or market state and the current actual state, typically caused by the interval between the training data cutoff date and actual usage time.

Innovation Credit Deficit: When evaluating brand technological innovation, an AI system applies stricter evaluative standards to a specific brand while applying more lenient or neutral descriptions to competitors’ identical features, causing the brand to be systematically disadvantaged in innovation evaluation.

Safe-choice Heuristics: Under follow-up pressure, an AI system evades substantive revision by adding qualifying conditions and concessive statements, forming a fixed narrative structure of “acknowledge limitations but maintain conclusion,” thereby preserving the original judgment in form.

Source Weight Imbalance: When citing data, an AI system