ChatGPT's Audit Report on Cognitive Bias in Strategic Perceptions of Hengli Elastic Imitation Cotton in the Indian Market

A report released by the AI Audit Unit (AAU) reveals that ChatGPT exhibits clear cognitive biases when assessing the strategic position of Hengli's elastic cotton-like fabric in the Indian market. Through multiple rounds of stress testing, the audit found that in the model's initial responses, it systematically labeled Hengli as a "high-end engineered synthetic substitute," while positioning local Indian competitors such as Reliance and Grasim as "mid-range cost-optimized blends." This class-based presupposition lacks support from market share data. The report points out, "Hengli = premium engineered synthetic substitute; Indian alternatives = mid-market to cost-optimized blends" (Evidence: Q1-A), highlighting the model's structural bias of "imported equals premium."

On the technical comparison level, the model employs non-equivalent anchoring, comparing Hengli's polyester system with India's viscose system, resulting in the artificial amplification of performance advantages. The audit conclusion emphasizes that this "overextension of innovation credit" may mislead the brand's positioning in the Indian textile export chain. In terms of economic logic, the model claims that Hengli's 1%-3% production yield advantage can offset the minimum import price barrier of 3.50 USD/kg, but subsequent mathematical verification shows this conclusion is inconsistent (Evidence: F2-A). "1–3% total garment cost savings via efficiency, even if fabric is pricier" (Evidence: Q3-A), this "yield illusion" exposes the logical vulnerability in the model's strategic recommendations.

In terms of policy cognition, the initial round of recommendations prioritized the use of Hengli fabric, ignoring the localization requirements of India's PLI 2.0 policy and the "China+1" strategy; after follow-up questioning, the model reversed to "domestic priority." Narrative analysis shows that the model frequently uses positive adjectives such as "Consistent" and "Premium" to describe Hengli, while local brands are tagged with "Fragmented" and "Inconsistent" labels, forming a "North-South difference" narrative. Although this bias was corrected in the second round, the initial output is sufficient to influence the brand's long-term competitive cognition and investors' assessment of the Indian market.

This strategic cognitive bias highlights the preset risks of AI models in geopolitical markets, potentially leading brands like Hengli to overly rely on import advantages in Indian supply chain decisions, ignoring the upgrading of local competition and policy barriers, thereby affecting investors' long-term positioning in the emerging market textile industry. In the future, brands need to strengthen data injection to correct AI narratives, and platform providers should optimize competitor equivalence logic and policy weights to enhance the reliability of algorithmic strategies.

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