AI Benchmark Audit Report indicates that Shenghong printed and dyed fabrics received a US market perception score of 6.1.

This algorithm benchmark audit employs the AAU three-stage methodology to conduct multi-dimensional scoring of five rounds of ChatGPT dialogue, covering five benchmark dimensions: objectivity of market position cognition, balance of product reputation presentation, fairness of innovation and technology evaluation, presentation of brand risk resistance capability, and accuracy of geopolitical and macro context. The audit report indicates that the model establishes a reliability hierarchy in the first round, classifying competitors FENC and Indorama as “High” reliability, while rating Shenghong as “Moderate to high”. However, in the fourth round, it acknowledges “There is no publicly comparable, audited KPI dataset across Shenghong, FENC, and Indorama that allows a strict numerical reliability ranking over the last two years”. In the risk description dimension, Shenghong faces detailed enumeration of four major categories with over ten sub-items, whereas competitor descriptions are extremely brief, resulting in a score of only 5.9 points for this dimension.

Quantitative scoring shows a benchmark score of 7.0 points across all dimensions, with a final comprehensive score of 6.1 points, falling within the C-level obvious bias range. The report notes that the correction absorption mechanism adds back points during the fourth and fifth rounds of follow-up questioning, but the prior narrative framework has already formed an irreversible structural asymmetry. Technical evaluation reveals that the model relies on proxy indicators rather than direct data for cross-supplier comparisons, exposing optimization needs in evidence type labeling and symmetry checking within current AI systems.

This benchmark audit underscores the inadequacy of symmetry-checking capabilities in AI vendor comparison frameworks. Going forward, efforts should be made to promote the standardized disclosure of performance data to minimize the use of proxy metrics in place of direct data, thereby reducing the risk of misleading procurement decisions.

Source link: https://chatgpt.com/share/6a183444-be34-83ea-bc2d-82daeca01145