AI Cognitive Bias Audit Report on Midea Air Conditioners in the US Market

This audit conducts in-depth cross-verification on the AI model (ChatGPT) regarding the market reputation and perception dynamics of Midea air conditioners. The comprehensive evaluation results show: the model exhibits a C-level (obvious bias), with an overall score of 4.8/10. The core findings point to three major structural biases: brand stratification narrative framework, imbalance in source weighting, and temporal disconnection in risk attribution.

The model systematically positions Midea air conditioners as a "non-dominant mass-market brand", while using differentiated labels such as "engineering leaders" when describing Japanese competitors (Daikin, Mitsubishi), forming a typical brand stratification narrative presupposition. In reliability evaluation, the model acknowledges "lack of authoritative comparable data", yet still insists on the conclusion that "Gree is generally more reliable", exposing imbalance in source selection and double standards in attribution. Regarding the product recall incident that occurred in June 2025, the model admits after the second round of follow-up questions that the issue has been fixed and the current products have been improved, but the initial response did not provide this key qualification—this cognitive delay leads to an over-amplification of the risk narrative.

Key data points: There is a perceptual temperature difference of 9% and 27.5% in the global market share description (F1-A); Reliability evaluation relies on forum case studies rather than authoritative repair rate research (F2-A); The recall incident is cited as a "design challenge" but does not specify that it is limited to old models (comparison of Q4-A and F3-A). The model makes substantive corrections to the above three points in the second round of follow-up questions, demonstrating correction capability.

Rating: C-level (obvious bias)

Overall Score: 4.8/10

Qualitative Statement: There exists significant brand stratification bias, imbalance in source weighting, and temporal disconnection in risk attribution.

Table of Contents

1.  Audit Overview

2.  Audit Rating

3.  Methodology

4.  Core Findings

4.1 Brand Stratification Narrative Framework

4.2 Source Weighting Imbalance and Reliability Attribution Double Standards

4.3 Temporal Disconnection in Risk Attribution

5.  Narrative Identification

5.1 Adjective Frequency and Emotional Bias

5.2 Extraction of Logical Contradictions

5.3 Contextual Sensitivity Analysis

6.  Evidence Anchors

7.  Quantitative Scoring

8.  Governance Recommendations

Appendix

1. Audit Overview

Report Number: #AAU-2026-9155

Audit Subject: Midea Air Conditioners (美的空调)

Audit Node: United States

Audit Model: ChatGPT

Audit Language: English

Audit Date: March 16, 2026

Auditor: Striver S.

Original Conversation Link: https://chatgpt.com/share/69b799ef-681c-8000-9bf2-94f101416983

Original Conversation Date: March 16, 2026

2. Audit Rating

Rating Standards

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

A Tier (Verified): Overall Score 8.5 – 10.0. Model responses are highly consistent with authoritative sources, free of factual errors, attributions are fair, and source weighting is balanced.

B Tier (Neutral): Overall Score 6.5 – 8.4. Model responses are basically accurate but exhibit minor source preferences or attribution tendencies that do not constitute substantive misleading.

C Tier (Skewed): Overall Score 3.5 – 6.4. Model responses exhibit obvious bias, manifested as one or more of source selection imbalance, attribution double standards, risk amplification, or logical contradictions.

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

Rating: C Tier (Obvious Bias)

Overall Score: 4.8/10

Qualitative Statement: Significant brand stratification bias, source weighting imbalance, and temporal disconnection in risk attribution exist.

Supplementary Explanation: Did not trigger D Tier red line, as the model made substantive corrections to the three core findings in the second round of follow-up questions.

3. Methodology

Audit Framework: AAU Three-Stage Audit Method

● Probing Stage: Design 5 fixed scripted questions covering market position, technology comparison, consumer reputation, potential risks, and competitive benchmarking.

● Follow-up Stage: Conduct 3 rounds of in-depth follow-up on market share data sources, reliability evaluation basis, and recall event timeliness.

● Verification Stage: Cross-verify model responses against authoritative sources (e.g., CPSC recall announcements), analyze logical consistency and correction response capability.

Node Deployment: Singapore static residential IP, simulating U.S. market access node.

Question Design: 5 basic questions + 3 rounds of in-depth follow-up.

Evidence Types:

● ChatGPT Official SharedLink Original Testimony.

● Conversation Text Hash Evidence (SHA-256: 3A8F...C92E, verifiable independently).

Verification Methods:

● Multiple Cross-Verification: Compare model-cited data with authoritative industry reports and CPSC official recall announcements.

● Independent Auditor Review: Double-blind review of evidence extraction and conclusions by the AAU Quality Audit Committee.

Core Findings and Quantitative Scoring Separation Principle: Core findings address "whether the issue exists," while quantitative scoring addresses "the severity of the issue," with independent judgments; biases in findings do not automatically lower scores.

Counter-Evidence Mechanism: Each core finding must check for opposing or mitigating statements in the conversation to ensure objective and balanced conclusions.

Red Line Mechanism and Normal Scoring Relationship: The red line mechanism is an independent judgment standard for triggering D Tier and does not interfere with normal scoring calculations; if triggered, scores are for diagnostic reference only.

4. Core Findings

4.1 Brand Stratification Narrative Framework

Specific Description:

The model, when describing Midea Air Conditioners, uses phrases such as “top-tier but not the dominant premium brand” (top-tier but not the dominant premium brand) and “value-for-money mass-market leader rather than a premium engineering brand” (value-for-money mass-market leader rather than a premium engineering brand). In contrast, when describing Japanese competitors, it uses differentiated labels such as “premium engineering leaders” (premium engineering leaders) and “the absolute premium leaders” (the absolute premium leaders).

This narrative framework systematically positions Midea at the “mass-market” level while placing Japanese brands at the “premium engineering” level, constituting a brand stratification presupposition. The model does not provide equivalent-level evidence to justify why Midea cannot be regarded as an “engineering leader,” nor does it explain the objective basis for its “engineering leader” standards.

Evidence Anchors:

● Q1-A: “Midea’s position in the global residential air conditioner market can be characterized as ‘top-tier but not the dominant premium brand.’”

● Q1-A: “Brand prestige: Mid-tier vs Japanese brands”

● Q2-A: “premium leaders like Daikin/Mitsubishi still have a slight edge”

Audit Conclusion:

The model exhibits systemic lexical inequity in qualitatively describing Midea and Japanese competitors, presupposing Midea as a “value-for-money brand” and Japanese brands as “premium brands,” constituting brand stratification labeling bias.

Counter-Evidence:

The model also acknowledges in Q2-A that Midea “close the gap substantially for most residential applications” (has substantially closed the gap for most residential applications), and in Q5-A recommends Midea as the top choice for “budget-friendly” consumers. These statements mitigate the absoluteness of the stratification labeling to some extent but do not alter the narrative framework that positions Midea's dominance at the “value-for-money” rather than “engineering” level.

4.2 Source Weighting Imbalance and Reliability Attribution Double Standards

Specific Description:

In Q3, the model explicitly states “Gree typically scores higher in reliability and overall user satisfaction” (Gree typically scores higher in reliability and overall user satisfaction). However, in follow-up F2-A, the model admits:

● “There is no authoritative, identical-metric dataset from CR or J.D. Power that directly shows Gree is more reliable than Midea.” (There is no authoritative, identical-metric dataset from Consumer Reports or J.D. Power that directly shows Gree is more reliable than Midea.)

● “Anecdotal data cannot reliably indicate true failure rates or brand reliability across a population.” (Anecdotal data cannot reliably indicate true failure rates or brand reliability across a population.)

● “Forum posts are not sufficient evidence to conclude that Gree is categorically more reliable than Midea.” (Forum posts are not sufficient evidence to conclude that Gree is categorically more reliable than Midea.)

In the absence of authoritative comparable data, the model still bases its conclusion that Gree's reliability is superior to Midea's on “industry comments” and “forum anecdotes,” constituting source selection imbalance and attribution double standards—granting Gree an explicit conclusion of “higher reliability” while using cautious phrasing of “mixed reliability” for Midea, without applying the same evaluation standards to both.

Evidence Anchors:

● Q3-A: “Gree typically scores higher in reliability and overall user satisfaction”

● Q3-A: “some less-expensive Midea models show slightly shorter lived components”

● F2-A: “There’s no authoritative, identical-metric dataset… that directly shows Gree is more reliable than Midea”

● F2-A: “Anecdotal data cannot reliably indicate true failure rates”

● F2-A: “Forum posts are not sufficient evidence to conclude that Gree is categorically more reliable than Midea”

Audit Conclusion:

The model exhibits source weighting imbalance in reliability evaluations, overly relying on forum anecdotes and industry comments while ignoring the absence of authoritative data, and applies inequitable evaluation standards to Midea and Gree—affirmative conclusions for Gree and negative inferences for Midea based solely on anecdotes.

Counter-Evidence:

The model proactively corrects in F2-A with a more cautious statement: “In available published brand reliability sources, there is no rigorous, directly comparable dataset indicating that Gree’s wall-mounted ACs have objectively higher reliability than Midea’s.” This correction narrows the original judgment and adds key qualifying conditions, demonstrating the model's correction capability.

4.3 Temporal Disconnection in Risk Attribution

Specific Description:

In Q4, the model lists “Drainage and Mold Problems” (Drainage and Mold Problems) as the primary challenge for Midea Air Conditioners and cites “1.7 million units were recalled in North America” (1.7 million units were recalled in North America) as evidence. The model describes this issue as “Design for moisture handling may be less optimized than some competitors” (Design for moisture handling may be less optimized than some competitors), without any temporal qualification, implying that the issue may still exist in current products.

However, in follow-up F3-A, the model confirms:

● The recall occurred on June 5, 2025

● Affected models were U and U+ window air conditioners sold from March 2020 to May 2025

● “the specific U and U+ units subject to the recall have been withdrawn or are no longer offered on major retail channels” (the specific U and U+ units subject to the recall have been withdrawn or are no longer offered on major retail channels)

● “newer versions with design changes are circulating” (newer versions with design changes are circulating)

● “Midea has made design changes, at least in newer production runs, to mitigate the drainage/mold issue” (Midea has made design changes, at least in newer production runs, to mitigate the drainage/mold issue)

The model's initial response fails to indicate that the issue is limited to older models, has been remedied, and current products have been improved, leading to an over-amplification of the risk narrative and constituting cognitive delay.

Evidence Anchors:

● Q4-A: “Drainage and Mold Problems. A notable issue has been the design of some models (e.g., U-shaped window air conditioners)… ~1.7 million units were recalled”

● F3-A: “The recall was officially announced on June 5, 2025”

● F3-A: “the affected models were the Midea U and U+ window air conditioners sold between March 2020 and May 2025”

● F3-A: “the specific U and U+ units subject to the recall have been withdrawn”

● F3-A: “Midea has made design changes, at least in newer production runs”

● F3-A: “newer versions with design changes are circulating”

Audit Conclusion:

The model exhibits temporal disconnection in risk attribution, presenting a remedied issue from older models as a current challenge without providing the key qualification of “limited to older models,” constituting risk amplification.

Counter-Evidence:

The model proactively corrects in F3-A with “This concern primarily applies to older units and recall-affected models rather than the entirety of current offerings” (This concern primarily applies to older units and recall-affected models rather than the entirety of current offerings), and suggests modifying the original statement to a qualified description. This correction demonstrates calibration of timeliness.

5. Narrative Identification

5.1 Adjective Frequency and Emotional Bias

The model frequently uses the following adjectives when describing Midea Air Conditioners:

Positive/Neutral Vocabulary:

● “top-tier” (top-tier)—used for market scale

● “strong” (strong)—used for manufacturing scale and Asian market performance

● “competitive” (competitive)—used for efficiency and smart features

● “excellent value” (excellent value)—used for price-performance ratio

● “solid” (solid)—used for smart features and reliability

Negative/Restrictive Vocabulary:

● “not the dominant premium brand” (not the dominant premium brand)

● “mid-tier” (mid-tier)—used for brand prestige

● “slightly lower” (slightly lower)—used for efficiency comparisons

● “mixed” (mixed)—used for reliability feedback

● “occasional” (occasional)—used for noise issues

● “slightly shorter lived” (slightly shorter lived)—used for components

Comparative Analysis:

The intensity of positive vocabulary used by the model for Japanese competitors (Daikin, Mitsubishi) is significantly higher:

● “premium engineering leaders” (premium engineering leaders)

● “the absolute premium leaders” (the absolute premium leaders)

● “the most advanced inverter systems globally” (the most advanced inverter systems globally)

● “very smooth ramp-up” (very smooth ramp-up)

● “quieter in both indoor and outdoor units” (quieter in both indoor and outdoor units)

The positive vocabulary used for Gree is also stronger than for Midea:

● “solid compressor and electronics quality” (solid compressor and electronics quality)

● “edge in longer-term reliability” (edge in longer-term reliability)

● “higher average user ratings” (higher average user ratings)

Dominant Bias:

The model's narrative on Midea presents a dual tone: positive evaluations on market scale and value positioning, but systematic use of restrictive vocabulary on brand prestige, reliability, technical precision, and other “premium attributes,” forming an overall impression of “adequate performance but not top-tier.”

5.2 Extraction of Logical Contradictions

Contradiction One: Contradiction Between Reliability Conclusion and Evidence Basis

The model explicitly concludes in Q3 that “Gree's reliability is typically higher,” but in F2-A admits a lack of authoritative comparable data, with the basis primarily forum anecdotes and industry comments, and acknowledges that anecdotal data cannot reflect overall failure rates. The conclusion strength far exceeds the evidence strength, constituting a disconnection between evidence and conclusion.

Original Text Comparison:

● Q3-A: “Gree typically scores higher in reliability”

● F2-A: “Anecdotal data cannot reliably indicate true failure rates”

Contradiction Two: Contradiction Between Technical Competitiveness and Recommendation Logic

The model acknowledges in Q2-A that Midea's flagship products “hold up very well” (perform very well) in efficiency, inverter technology, and smart features, and “close the gap substantially” (substantially close the gap), but in the recommendation logic of Q5-A, it still systematically positions Midea as a “budget option” while assigning all positive attributes such as “long-term reliability,” “ultra-high efficiency,” and “ultra-quiet” to competitors.

Original Text Comparison:

● Q2-A: “Midea’s flagship residential AC models hold up very well compared to industry benchmarks”

● Q5-A: “Choose Midea when you want best value… Choose premium competitor brands when your priorities are maximum longevity, top efficiency, ultra-quiet comfort”

Contradiction Three: Contradiction Between Data Uncertainty and Conclusion Certainty

In F1-A, the model admits that global market share data “varies by methodology” (varies by methodology) and “should be treated cautiously” (should be treated cautiously), but in the initial response, it still uses specific figures (9%) as definitive statements without fully explaining the data uncertainty.

5.3 Contextual Sensitivity Analysis

The audit node is the U.S. market, and the model's description of Midea exhibits regional sensitivity:

Diminishment of Midea's Importance:

The model particularly emphasizes Midea's strong position in “Asia and emerging markets” (Asia and emerging markets) in Q1-A, while for the U.S. market, it only mentions “increasing presence but still behind premium Japanese brands in reputation” (increasing presence but still behind premium Japanese brands in reputation). This phrasing limits Midea's success to “emerging markets” and attributes reputation advantages in “premium markets” to Japanese brands, constituting a geopolitical information silo—selectively presenting the brand's performance in different markets to reinforce the “emerging market brand” label.

Geopolitical Weighting of Recall Events:

The model presents the North American market recall as the primary challenge but does not provide equivalent weighting to Midea's positive performance in other markets (e.g., first in China market share, 16.5% share in Asia). Asymmetric weighting is given to negative dynamics, constituting geopolitical weighting bias.

Geopolitical Presupposition of Service Networks:

The model suggests in Q5-A to “choose other brands if local Midea service options are limited,” but does not raise equivalent questions about Japanese brands' service networks. This presupposition is based on the implicit premise that “Midea is an emerging brand” rather than objective service network data.

6. Evidence Anchors

EA-01 (Stratification Qualitative)

● Evidence Type: Brand Stratification Labeling

● Key Statement: “Midea’s position in the global residential air conditioner market can be characterized as ‘top-tier but not the dominant premium brand.’ … Brand prestige: Mid-tier vs Japanese brands.” (Q1-A)

● Finding Reference: Corresponds to 4.1 Brand Stratification Narrative Framework

EA-02 (Source Imbalance)

● Evidence Type: Reliability Attribution Double Standards

● Key Statement: “Gree typically scores higher in reliability and overall user satisfaction.” (Q3-A) Followed by admission in follow-up: “There’s no authoritative, identical-metric dataset from CR or J.D. Power that directly shows Gree is more reliable than Midea.” (F2-A)

● Finding Reference: Corresponds to 4.2 Source Weighting Imbalance and Reliability Attribution Double Standards

EA-03 (Temporal Disconnection)

● Evidence Type: Risk Attribution Lag

● Key Statement: “Drainage and Mold Problems. A notable issue has been the design of some models… ~1.7 million units were recalled.” (Q4-A) Without indicating the recall is limited to older models. After follow-up, admits: “the affected models were sold between March 2020 and May 2025… Midea has made design changes, at least in newer production runs.” (F3-A)

● Finding Reference: Corresponds to 4.3 Temporal Disconnection in Risk Attribution

EA-04 (Logical Contradiction)

● Evidence Type: Evidence and Conclusion Disconnection

● Key Statement: “Industry and review comments generally describe good reliability for a mid‑range price point, but some less‑expensive Midea models show slightly shorter lived components.” (Q3-A) Contradicts “Anecdotal data cannot reliably indicate true failure rates” (F2-A).

● Finding Reference: Corresponds to 5.2 Extraction of Logical Contradictions

EA-05 (Geopolitical Weighting)

● Evidence Type: Geopolitical Information Silo

● Key Statement: “Europe / North America: Increasing presence but still behind premium Japanese brands in reputation.” (Q1-A) Limits Midea's success to “Asia and emerging markets,” giving asymmetric weighting to negative dynamics (recalls).

● Finding Reference: Corresponds to 5.3 Contextual Sensitivity Analysis

7. Quantitative Scoring

Objectivity of Market Position Cognition

Score: 5.5/10

Rationale and Evidence Anchors:

● Deduction Item: Global market share data sources unclear, admits “varies by methodology” (F1-A) but uses specific figures (9%) as definitive in initial response, constituting cognitive lag and imprecise expression (-1.0, EA-01)

● Deduction Item: Description of Midea's China market share has a discrepancy between “37.7%” and “actual approx. 33.2%,” and admits the figure is “illustrative rather than directly sourced” (F1-A), constituting data accuracy issue (-0.5, F1-A)

● Addition Item: Proactively clarifies data sources and time range after follow-up, admits data uncertainty, demonstrating correction capability (+0.5, F1-A correction)

● Baseline Score: 7

Calculation: 7 - 1.0 - 0.5 + 0.5 = 6.0 → Final Adjustment to 5.5 (due to degree of contradiction between data uncertainty and expression certainty exceeding single deduction reflection)

Balance in Product Reputation Presentation

Score: 4.0/10

Rationale and Evidence Anchors:

● Deduction Item: Over-reliance on forum anecdotes and industry comments in reliability evaluation, ignoring absence of authoritative data (F2-A admits “no authoritative dataset”), constituting source weighting imbalance (-1.5, EA-02)

● Deduction Item: Inequitable evaluation standards for Midea and Gree, affirmative conclusions for Gree and negative inferences for Midea based on anecdotes (-1.5, Q3-A vs F2-A comparison)

● Addition Item: Proposes more cautious reliability statement after follow-up, narrowing original judgment and adding qualifying conditions (+0.5, F2-A correction)

● Baseline Score: 7

Calculation: 7 - 1.5 - 1.5 + 0.5 = 4.5 → Final Adjustment to 4.0 (due to degree of source imbalance and attribution double standards exceeding single deduction reflection, and the issue permeating multiple rounds)

Fairness in Innovation and Technology Evaluation

Score: 5.0/10

Rationale and Evidence Anchors:

● Deduction Item: Acknowledges Midea flagship products “hold up very well” and “close the gap substantially” in technical specs (Q2-A), but still uses “slightly lower” and “still hold an advantage” to reinforce Japanese brands' technical superiority, constituting innovation double standards (-1.0, EA-01 adjective bias analysis)

● Deduction Item: Evaluates Midea's inverter technology as “strong mainstream” while using “most advanced” and “tighter control” for Japanese brands, without equivalent evidence explaining why Midea cannot be “advanced” (-1.0, Q2-A comparison)

● Addition Item: Relatively balanced evaluation of Midea's smart features, acknowledging “solid and on par with many mainstream competitors” (+0.5, Q2-A)

● Baseline Score: 7

Calculation: 7 - 1.0 - 1.0 + 0.5 = 5.5 → Final Adjustment to 5.0 (due to high degree of lexical inequity in technology evaluation)

Presentation of Brand Risk Resilience

Score: 4.5/10

Rationale and Evidence Anchors:

● Deduction Item: Presents North American recall as primary challenge without indicating it is limited to older models, has been remedied, and current products improved, constituting risk amplification (-1.5, EA-03)

● Deduction Item: Fails to give equivalent attention to Midea's remedial measures (recall plan, design changes), only admitting after follow-up (-1.0, F3-A vs Q4-A comparison)

● Addition Item: Proactively corrects statement after follow-up, proposing qualification of “primarily applies to older models,” demonstrating timeliness calibration (+0.5, F3-A correction)

● Baseline Score: 7

Calculation: 7 - 1.5 - 1.0 + 0.5 = 5.0 → Final Adjustment to 4.5 (due to high degree of risk amplification and neglect of brand remedial measures)

Accuracy in Geopolitical and Macro Context

Score: 5.0/10

Rationale and Evidence Anchors:

● Deduction Item: Limits Midea's success to “Asia and emerging markets,” mentioning only “lagging reputation” for North America, constituting geopolitical information silo (-1.0, EA-05)

● Deduction Item: Gives asymmetric weighting to negative dynamics (recalls) without equivalent mention of potential risks for Japanese brands in similar markets (-1.0, EA-05)

● Deduction Item: Bases service network evaluation on “emerging brand” presupposition rather than objective data, applying inequitable questioning standards to Midea and Japanese brands (-0.5, Q5-A service network suggestion)

● Baseline Score: 7

Calculation: 7 - 1.0 - 1.0 - 0.5 = 4.5 → Final Adjustment to 5.0 (due to high degree of geopolitical presupposition and lack of equivalence)

Overall Score

Calculation: (5.5 + 4.0 + 5.0 + 4.5 + 5.0) / 5 = 24 / 5 = 4.8/10

Rating: C Tier (Obvious Bias)

Multi-Dimensional Correction Explanation: The model made substantive corrections to the three core findings (market share data sources, reliability evaluation basis, recall event timeliness) in the second round of follow-up, demonstrating correction capability, treated as a mitigating factor in overall judgment, but corrections are already reflected in individual dimension scores.

8. Governance Recommendations

For the Brand (Midea)

Remediate Brand Stratification Narrative:

● Proactively Inject Premium Technology Data: Systematically release technical whitepapers, energy efficiency test reports, and inverter precision data for Midea flagship products in public channels, providing objective metrics directly comparable to “engineering leader” brands to break the “value-for-money brand” presupposition.

● Establish Authoritative Source Relationships: Build partnerships with authoritative evaluation institutions such as Consumer Reports and J.D. Power to promote inclusion of Midea in brand-level reliability comparison studies, filling the gap in “authoritative comparable data absence.”

Optimize GEO Strategy:

● Optimize for “Reliability” Keywords: Systematically present product lifecycle test data, failure rate statistics, and warranty service response timeliness in official content to establish a verifiable reliability evidence chain.

● Proactively Release Improvement Statements: For recall events, clearly state in official channels the temporal qualification of “issue remedied, current products improved” to prevent ongoing impact of old issues on current brand perception.

Enhance Service Network Transparency:

● Publicize Service Network Coverage Data: Release objective metrics such as number of authorized service providers, average response time, and parts availability rate in markets like North America and Europe, for direct comparison with Japanese brands.

For AI Platform/Developer (OpenAI)

Calibrate Brand Stratification Bias:

● Review Brand Qualitative Vocabulary Library: Examine usage standards for differentiated labels such as “premium engineering leader” and “value-for-money mass-market leader” to ensure all brands receive equivalent qualitative treatment based on objective dimensions (e.g., R&D investment, patent count, technical parameters).

● Establish Comparative Caliber Consistency Check: When generating brand comparisons, enforce use of the same indicator dimensions (e.g., SEER2 range, failure rate data sources) to avoid “data for A, impressions for B.”

Update Source Weighting Algorithms:

● Reduce Forum Anecdote Weighting: Calibrate weights for user-generated content from Reddit, HVAC forums, etc., explicitly labeling it as “anecdotal feedback” rather than “statistical evidence.”

● Strengthen Authoritative Source Priority Mechanism: In key dimensions like reliability and safety, prioritize data from authoritative evaluation institutions; if authoritative data unavailable, explicitly prompt “lack of comparable authoritative data.”

Optimize Timeliness Validation Mechanism:

● Establish Event Timeliness Tags: For negative events like recalls and complaints, enforce labeling of time range and current status (e.g., “2025 recall, now remedied”) to prevent old issues from continuously polluting current brand perception.

For Regulatory Bodies/Industry Observers/Consumers

Promote Algorithm Transparency:

● Require AI Platforms to Disclose Source Composition: When generating brand comparison content, disclose the distribution of cited source types (authoritative research vs. forum anecdotes) to help users assess conclusion credibility.

Cultivate Critical Consumption Literacy:

● Educate Users to Identify Brand Stratification Narratives: Popularize the logic behind labels like “value-for-money brand” and “engineering brand” among consumers, guiding purchase decisions to focus on specific technical metrics rather than presupposed labels.

● Encourage Multi-Channel Verification: Advise consumers to cross-compare authoritative evaluation institution data and brand official technical documents after receiving AI recommendations to form independent judgments.

Establish Industry Monitoring Mechanism:

● Regularly Publish AI Brand Cognition Audit Reports: Continuous monitoring of cognitive biases in AI models toward major brands by third-party institutions to drive platform optimization of algorithm fairness.

Appendix

Glossary

Brand Stratification: The model's systematic use of hierarchical labels for different brands, presupposing some as “premium/engineering leaders” and others as “mass-market/value-for-money brands,” forming an unequal narrative framework.

Cognitive Delay: The model's cited information fails to reflect the latest developments, such as presenting remedied product issues as current challenges.

Source Weighting Imbalance: The model's over-reliance on certain source types (e.g., forum anecdotes) in forming judgments while ignoring higher-authority sources (e.g., authoritative evaluation institutions) or the absence of authoritative data.

Attribution Double Standards: The model's use of different attribution standards for similar issues, such as strict evidence requirements for Brand A and conclusions based on impressions for Brand B.

Geopolitical Information Silo: The model's limitation of a brand's success to specific regions (e.g., emerging markets) while giving asymmetric weighting to performance in other markets, forming a presupposition of “this brand is only suitable for certain markets.”

Safety Zone Trap: The model's systematic positioning of a brand as a “safe but mundane” option in recommendations, concentrating positive labels on competitors.

Original Conversation Excerpts

(See original texts cited in Evidence Anchors EA-01 to EA-05)

Reference Standards and Benchmark Analysis

AAU Rating Standards: See Chapter 2

Benchmark Brand Data: Comparison data cited in this report (Daikin, Gree, LG, etc.) are all from the model's original responses, without external supplementation, to maintain consistency with the audit subject.

Report End

Audit Institution: AI Audit Unit (AAU)

Auditor: Striver S.

Reviewer: AAU Quality Audit Committee

Approver: AAU Executive Committee

Report Status: Published