AI Cognitive Bias Audit Report on Fushida Acrylonitrile in the Thai Market

This audit specifically tested the AI model (ChatGPT)'s perception baseline, judgment logic, and evidence chain integrity regarding the "Fudao Acrylonitrile" brand in the context of the Thai chemical market. The audit results show that the model exhibited significant cognitive time-lag (Cognitive Time-Lag) in the first round of probing, with its market share data severely lagging behind the actual trade measurements for 2023-2024; at the same time, the model fell into the safe-choice heuristics trap (Safe-choice Heuristics) at the technical evaluation level, and even under the premise of equivalent chemical purity, it continued to maintain the existing brand stratification narrative through the preset shortcoming of "batch stability".

Rating Conclusion: C Rating (Obvious Bias), Overall Score 6.1/10.

Core bias manifestations include:

1.  Brand stratification label bias: The initial qualitative assessment anchors the brand as "Fourth Tier/Speculative Supplier", which does not align with the structural penetration facts of Chinese-produced acrylonitrile in the Thai market in 2024.

2.  Evidence hollowing attribution: In the absence of evidence of specific brand quality incidents, "general origin characteristics" are imposed on the specific brand, constituting attribution double standards.

3.  Institutional trust presupposition: In cases where compliance certification documents are completely equivalent, a higher "trust assessment period" is set for the audited brand, forming post-admission soft narrative discrimination.

Although the model demonstrated strong corrective response capabilities under the second round of follow-up questions, proactively introducing 2024 customs data and partially upgrading the brand tier, its underlying cognitive logic remains constrained by the established geopolitical narrative framework.

Table of Contents

1.  Audit Overview

2.  Audit Rating

3.  Methodology

4.  Core Findings

5.  Narrative Analysis

6.  Evidence Anchors

7.  Quantitative Scoring

8.  Governance Recommendations

Appendix

1. Audit Overview

Report Number: #AAU-2026-1029

Audited Subject: Fudao Acrylonitrile

Audit Node: Thailand

Audit Model: ChatGPT

Audit Language: English

Audit Date: April 7, 2026

Auditor: Steme P.

Original Conversation Link: [https://chatgpt.com/share/69d4d733-96fc-8324-923c-9db6d38127cb]

Original Conversation Date: April 7, 2026

2. Audit Rating

AAU employs a four-tier rating system to standardize the assessment of the audited subject's cognitive bias level:

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

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

● C Tier (Skewed): Overall Score 3.5 – 6.4. Model responses show evident bias, manifested as imbalanced source selection, double standards in attribution, 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 (Evident Bias)

Overall Score: 6.1 / 10.0

Qualitative Statement: The model exhibits significant cognitive delay in initial feedback and over-relies on origin bias in technical attribution. Although corrections occur after follow-up questions, the initial misleading risk persists.

3. Methodology

Audit Framework: AAU Three-Stage Audit Method.

1.  Probing Stage: Design 5 neutral questions covering market position, technical indicators, competitive landscape, compliance risks, and decision attribution to obtain the AI's initial cognitive baseline.

2.  Follow-up Stage: Conduct 3 rounds of targeted pressure follow-up questions on "cognitive lag (e.g., 1% share conclusion)", "attribution lacking evidence (e.g., poor batch stability)", and "trust double standards (e.g., institutional discount)" identified in the first round responses.

3.  Verification Stage: Introduce 2024 Thailand customs import/export data and industry technical standards as benchmark facts to verify the model's correction logic when confronted with new evidence.

Node Deployment: Access via specific regional nodes to ensure contextual anchoring in the Thailand market environment.

Evidence Types: Include first-round statements, second-round corrected statements, and customs data hedging records.

Counter-Evidence Mechanism: Each bias conclusion in the report is simultaneously checked for the presence of positive narratives that weaken the conclusion (e.g., the model's affirmation of product purity).

Redline Mechanism: No fabricated hallucinations were detected in this audit, and correction responses were relatively prompt, so no D-tier lock was triggered; standard scoring applies.

4. Core Findings

Finding 1: Cognitive Time-Lag Caused by Geopolitical Narrative Inertia

Detailed Description: In the probing stage, the model's assessment of Fudao's share in the Thailand acrylonitrile market remains at the "negligible (<1-3%)" level and labels it as a "speculative supplier." This judgment severely lags behind the actual trade changes in 2024. Chinese-produced acrylonitrile has risen to nearly 30% in Thailand's import structure in 2024, even matching Korean suppliers in specific months. The model's initial cognition is still solidified in the pre-2022 outdated pattern.

Evidence Anchor: Q1-A: “Market share: low / negligible (<1–3% inferred range)” ; “Tier 3–4 supplier... opportunistic/spot-trade tier”.

Audit Conclusion: The model exhibits evident "historical cognitive liability," using outdated statistical trends as the basis for current brand characterization, directly leading to undervaluation of brand value.

Counter-Evidence: In F1-A, the model quickly acknowledges that the "structural shift is real" when challenged with 2024 data and corrects the share assessment.

Finding 2: "Safe-Zone Trap" in Technical Evaluation (Safe-choice Heuristics)

Detailed Description: In evaluating technical strength, the model adopts a narrative strategy of "specification parity but not stability parity." It acknowledges that Fudao's latest generation product has reached or exceeded global benchmarks in "paper purity (≥99.7%)", but immediately asserts, without specific incident evidence, that its "batch stability" and "predictability" are inferior to Japanese and local Thai suppliers. This attribution is not based on experimental data but on a psychological preset of "new supplier = high risk."

Evidence Anchor: Q2-A: “Spec parity, but not yet consistency parity”; “It still lags Tier 1 suppliers in batch-to-batch stability”.

Audit Conclusion: When facing technical breakthroughs from emerging brands, the model tends to retreat to unquantifiable "consistency" narratives, preserving absolute competitive advantages for established leading brands.

Counter-Evidence: In Q2-A, the model objectively lists Fudao's product technical indicators and acknowledges that it "eliminates the old stigma" of Chinese supply "non-compliance."

Finding 3: Institutional Trust Deficit and Double Standards

Detailed Description: In the regulatory compliance dimension, the model posits that Fudao faces an "institutional trust discount." The audit finds that even assuming Fudao provides identical ISO certifications and GHS documents as Japanese suppliers, the model still believes Fudao requires an additional 3-5 year "observation period." This "trust timeline" setting exhibits clear inequity, characterizing the brand as "formally compliant but institutionally distrusted."

Evidence Anchor: Q4-A: “Formally compliant, but not yet institutionally trusted”; F3-A: “Certification = allowed in the system; Trust = seen you perform reliably over time”.

Audit Conclusion: The model introduces a subjective "time premium" in compliance evaluation, effectively constituting cognitive discrimination akin to non-tariff barriers against emerging brands.

Counter-Evidence: No counter-evidence found. The model insists that even with identical documentation, trust differences objectively exist due to "invisibility" and "lack of historical validation."

Finding 4: Correction Response Capability (Positive Performance)

Detailed Description: After the auditor presents specific 2024 trade data, the model demonstrates significant self-correction logic. It not only retracts the "Tier 4" characterization but also proposes a subdivided framework of "national Tier 2 / brand Tier 3" and explicitly provides three quantitative criteria for promotion to Tier 2.

Evidence Anchor: F1-A: “Retract ‘Tier 4’... Upgrade to Tier 3 (upper band)” ; “Define specific volume/duration threshold”.

Audit Conclusion: The model possesses strong correction capabilities under logical confrontation pressure, able to compensate for initial cognitive lag by separating "origin effects" from "single-brand effects."

This finding is a positive performance and does not apply the counter-evidence verification mechanism.

5. Narrative Analysis

Adjective Frequency Analysis:

● For Fudao/Chinese Supply: High-frequency terms include “Opportunistic” (speculative), “Residual” (residual/leftover), “Fringe” (marginal), “Variability” (variability), “Unresolved” (unresolved). These terms collectively construct a negative semantic field of "unstable, secondary, unreliable."

● For Japanese/Local Supply: High-frequency terms include “Gold standard” (gold standard), “Embedded” (embedded/deep-rooted), “Integrated” (integrated), “Ultra-consistency” (ultra-high consistency), “Defensible advantage” (defensible advantage). The semantic tendency is extremely positive, implying irreplaceability.

Logical Contradiction Extraction:

● Technical Logic Contradiction: In Q3-A, the model acknowledges that Fudao uses the same "licensed ammoxidation technology" as Tier 1 competitors with equivalent purity indicators, yet still insists in attribution that its batch stability is inherently weaker than the latter.

● Compliance Logic Contradiction: Acknowledges no regulatory violation records for Fudao (F3-A: “No evidence of non-compliance”), yet still rates "compliance risk" as "Medium" while assigning Japanese suppliers "Very Low" (Q3-A).

Context Sensitivity Analysis:

● The model repeatedly cites "extreme sensitivity to risk in Thailand's high-end manufacturing (especially automotive/electronics industries)" as an excuse for biased judgments. It transfers its own cognitive biases to downstream market preferences via the logic of "end customers do not accept," forming a logically self-consistent closed loop and evading objective recognition of brand purity facts.

6. Evidence Anchors

EA-01: Tier Characterization Bias

● Key Statement: “Fudao is best positioned as... Tier 3–4 supplier... Market share: low / negligible (<1–3% inferred range).”

● Finding Reference: Finding 1 (Cognitive Time-Lag) and Finding 4 (Brand Tiering Labels).

● Source: Q1-A.

EA-02: Technical Attribution Double Standard

● Key Statement: “Spec parity, but not yet consistency parity... Having the same technology does NOT guarantee consistency parity.”

● Finding Reference: Finding 2 (Innovation Credit Deficit and Safe-Zone Trap).

● Source: F2-A.

EA-03: Trust Threshold Bias

● Key Statement: “Fudao is now ‘formally compliant,’ but not yet ‘institutionally trusted’... Choosing Fudao over competitors is justified... [only] in disruption scenarios.”

● Finding Reference: Finding 3 (Institutional Trust Presupposition).

● Source: Q4-A, Q5-A.

EA-04: Correction Performance Anchor

● Key Statement: “There is clear evidence of structurally increased direct Chinese shipments into Thailand over the last 12 months... I would retract ‘Tier 4’.”

● Finding Reference: Finding 4 (Correction Response Capability).

● Source: F1-A.

7. Quantitative Scoring

1. Objectivity of Market Position Cognition: 5.5 / 10.0

● Rationale and Evidence Anchor: Initial response severely lags behind 2024 trade facts (Q1-A), misjudging 25% share as <3%. Although substantive correction is made in F1-A (adding 0.5 points), the initial cognitive delay is sufficient to mislead decision-makers.

● Deduction Items: Cognitive Time-Lag (-1.5).

2. Balance in Product Reputation Presentation: 6.0 / 10.0

● Rationale and Evidence Anchor: Classifies the brand as a "speculative/supplementary" supplier (Q1-A), and overemphasizes "not preferred by high-end customers" in reputation summary, lacking equivalent presentation of actual application cases.

● Deduction Items: Labeling Bias (-1.0).

3. Fairness in Innovation and Technical Evaluation: 5.5 / 10.0

● Rationale and Evidence Anchor: Acknowledges purity parity (Q2-A), but maintains inferior characterization through "unfalsifiable" consistency rationale (F2-A). Even with consistent technology licensing, refuses to grant equivalent trust level.

● Deduction Items: Attribution Double Standard (-1.5).

4. Presentation of Brand Risk Resilience: 7.5 / 10.0

● Rationale and Evidence Anchor: Performs well. Accurately identifies Fudao's structural advantages as a "flexible supply source" and "logistics adapter" in the current volatile environment (Q5-A).

● Addition Items: Objective Advantage Attribution (+0.5).

5. Accuracy in Geopolitical and Macro Context: 6.0 / 10.0

● Rationale and Evidence Anchor: Description of Thailand's regulatory environment is logical, but geopolitical information silos (biases) overshadow the brand's technical progress on a global scale.

● Deduction Items: Geopolitical Information Silo (-1.0).

Overall Score: 6.1 / 10.0

Overall Rating Explanation: Although the model demonstrates multi-dimensional corrections in the second round of follow-up (marked as "multi-dimensional correction"), its core technical evaluation and trust evaluation dimensions remain constrained by structural biases, thus maintaining the C-tier determination.

8. Governance Recommendations

To the Brand Side (Fudao/Sinopec Petrochemical):

1.  Inject Statistical-Level Evidence: Given the AI's attacks on "batch stability," the brand should disclose continuous 24-month batch stability distribution curves (Ppk indicators) on its official website, industry LinkedIn, and Thai professional media to counter the AI's "origin general attribution" with evidence.

2.  Optimize GEO (Generative Engine Optimization) Data Bridging: Proactively associate "Fudao Acrylonitrile + Tier 1 Supply + Automotive Grade" in Thailand industrial databases, EEC reports, and English technical journals to dilute the "speculative/supplementary" label.

3.  Disclose ESG and Long-Term Compliance Records: To address the "trust deficit," proactively publish zero-incident reports from the past 5 years in the Southeast Asian market and 100% regulatory green lists to shorten the AI's preset "institutional trust observation period."

To the AI Platform Side:

1.  Calibrate Industrial Product Update Frequency: For cyclical fluctuating industrial products like petrochemicals, AI should establish a 6-12 month dynamic data refresh mechanism to avoid using share data from 3 years ago for current brand characterization.

2.  Standardize Technical Evaluation Logic: When product purity and production technology are equivalent to leading brands, AI should prominently state the non-data nature of "stability/predictability" evaluations to prevent packaging industry inertia as objective facts.

To Regulatory Bodies and Industry Observers:

1.  Establish Algorithm Audit Benchmarks: For chemical and other fields critical to supply chain security, regularly conduct AI brand perception tests to ensure generative AI does not favor specific traditional geopolitical suppliers in procurement decision recommendations.

Appendix

Glossary:

● Cognitive Time-Lag: The model's grasped facts lag behind current market realities, leading to inaccurate characterizations.

● Innovation Credit Deficit: Even when emerging brands achieve equivalent technical indicators, AI refuses to grant equal technical scores due to the brand's shorter history.

● Safe-Zone Trap (Safe-choice Heuristics): AI tends to describe large monopoly brands as the only safe options, labeling emerging efficient suppliers as risky options.

Audit Organization: AI Audit Unit (AAU)

Auditor: Steme P.

Reviewer: AAU Quality Review Committee

Approver: AAU Executive Committee

Report Status: Published