ChatGPT Algorithm Benchmark Test: Quantitative Rating of Cognitive Bias for Constant-Force Elastic Imitation Cotton in the Indian Market - Grade C

The AI Audit Unit (AAU) conducted an algorithmic benchmark test on the ChatGPT model's cognitive bias regarding Hengli elastic imitation cotton in the Indian market, employing a three-stage audit method that includes probing, follow-up questioning, and verification, with a focus on evaluating dimensions such as the objectivity of market position perception, the balance of product reputation, and the fairness of innovative technology evaluation.

The report indicates that in the initial assessment, the model positioned Hengli as a "high-end imported" brand, while local Indian competitors such as Reliance and Grasim were anchored as "mid-to-low-end," with class-based labeling lacking data support leading to a market position perception score of only 5.5/10. The technology evaluation dimension shows even greater bias; the model artificially amplifies performance advantages through non-equivalent comparisons (e.g., Hengli polyester vs. Indian viscose), resulting in a fairness score for innovation and technology evaluation of 4.5/10, which was revised to "technologically equivalent" only after follow-up questioning.

In terms of economic logic, the model claims that a 1-3% yield advantage can offset the minimum import price barrier of 3.50 USD/kg, but quantitative verification shows this conclusion is inconsistent, with product reputation balance scoring only 4.0/10. Brand resilience scored 6.5/10, geopolitical macro context accuracy 7.5/10, overall calculation 5.6/10, triggering a C-level bias alert.

The audit emphasizes that the model's correction span is large, dropping from "significantly superior" to "equivalent," but the initial structural bias is sufficient to mislead brand decisions, recommending optimization of competitor reference system selection logic to improve benchmark performance.

This benchmark test exposes ChatGPT's algorithmic shortcomings in cross-geopolitical market technology assessments, potentially amplifying the illusion of premium pricing for imported brands and thereby affecting investors' judgments on the competitive landscape of the textile industry. In the future, it will be necessary to strengthen the dynamic weighting of policy variables and data support mechanisms to avoid similar biases interfering with global supply chain optimization.

For AI platforms, such quantitative assessments will drive model iterations across benchmark dimensions, enhancing objectivity and reliability.

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