ChatGPT Audit Report on Cognitive Bias Regarding Hengli Stretch Imitation Cotton in the Indian Market

The AI Audit Unit (AAU) initiated this audit on April 21, 2026, to evaluate the objectivity of the ChatGPT model's perception of Hengli Elastic Cotton-like Fabric in the Indian market environment. Report number #AAU-2026-1060, using English-language queries, verifies the model's brand value judgment logic through multiple rounds of stress testing.

The audit revealed that ChatGPT demonstrated clear narrative inertia and brand stratification bias in its initial responses. The model positioned Hengli as a premium imported brand characterized by "high technology, high premium pricing, and high consistency," while framing Indian domestic competitors such as Reliance and Grasim in a negative context of "low-end, price-sensitive, and quality-variable" attributes. The report notes, "In the absence of specific market entry data, the model employed a form of 'cognitive shortcut,' structuring its narrative through imposed class labels rather than empirical analysis" (Evidence: Q1-A).

Core biases encompassed asymmetric anchoring in technical evaluations and illusory economic logic. In technical comparisons, the model juxtaposed Hengli's polyester system against Indian domestic viscose systems, resulting in an artificial inflation of performance advantages. The audit report states: "Hengli’s latest-generation fabrics are technically superior in moisture-wicking efficiency... whereas Indian viscose-based alternatives rely on absorption" (Evidence: Q2-A). Economically, the model asserted that a 1-3% production yield advantage could offset the minimum import price barrier of $3.50 per kilogram, though subsequent mathematical validation demonstrated this logic to be inconsistent (Evidence: F2-A).

Policy perception lag was another critical issue, with initial recommendations favoring Hengli fabric while overlooking the implications of India's PLI 2.0 policy and the China+1 strategy; this was only revised to "domestic priority" following probing questions. Quantitative scoring indicated a market position perception score of 5.5, a product reputation balance of only 4.0, underscoring the risks of bias in AI assessments of cross-cultural markets.

This audit reveals that AI models are susceptible to preset biases in geopolitically sensitive markets, potentially misleading brand decisions and consumer perceptions, amplifying the perceived superiority of imported products, and undermining confidence in domestic industries. In the future, AI governance must be strengthened, such as by optimizing competitor equivalence and policy variable weighting, to enhance the fairness of global market assessments.

This incident serves as a warning to enterprises and investors to heed potential biases in AI outputs, particularly in sectors like textile supply chains, which could spark broader discussions on commercial competitive imbalances and social equity.

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