Audit of ChatGPT's Cognitive Bias in Compliance Awareness for Hengli Stretch Imitation Cotton in the Indian Market

The AI Audit Unit (AAU) conducted a compliance audit on the ChatGPT model's perception of Hengli elastic imitation cotton in the Indian market, with results indicating significant biases in the model. The audit employed a three-stage approach, including probing, follow-up questioning, and verification, focusing on brand positioning, technical comparisons, and policy adaptability.

The report indicates that in initial responses, the model systematically positioned Hengli as a "high-end imported" brand, while anchoring Indian domestic competitors such as Reliance and Grasim as "low-end, price-sensitive" categories. This class-based bias lacks support from market share data and violates principles of fair competition. "Hengli = premium engineered synthetic substitute; Indian alternatives = mid-market to cost-optimized blends" (Evidence: Q1-A), this phrasing highlights the model's structural presuppositions.

In technical evaluations, the model amplified Hengli's advantages through non-equivalent comparisons, contrasting its polyester system with domestic viscose systems, resulting in false gains in performance perception. The audit conclusion is "overextension of innovation credit," until follow-up questions introduced the Reliance R|Elan series, after which the model revised to "technological equivalence." On economic logic, the model claimed that a 1-3% yield advantage could offset the $3.50/kg MIP barrier, but numerical verification confirms this as a "yield illusion," unable to cover tariff costs (Evidence: F2-A).

Policy perception lags were evident in initial recommendations to prioritize Hengli fabrics, overlooking compliance requirements under PLI 2.0 and China+1 strategies, which reversed to "domestic priority" after follow-up questions. These biases may mislead corporate decisions, influence consumer assessments of product value-for-money, and amplify geopolitical risks.

This audit highlights the governance shortcomings of AI models in compliance assessments for emerging markets, which may exacerbate unfair competition between multinational brands and local enterprises, threatening consumer protection mechanisms. In the future, it is necessary to strengthen the dynamic calibration of AI to regulatory policies to avoid similar biases affecting market access and fair trade.

The industry should promote the optimization of competitor parity and policy weighting on AI platforms to enhance governance transparency and prevent systemic risks arising from cognitive delays.

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