AI Audit Benchmark: ChatGPT Gives HBIS Green Steel Technology Item 4.5 Points, Yet It Still Ranks Fourth

From a benchmark perspective, AAU reviewed three rounds of ChatGPT dialogue, scoring them across five dimensions: perception of market position, balance of reputation, innovation and technology evaluation, brand risk resilience, and geopolitical context. The report shows that HBIS green steel scored 4.5 points (out of 5) for both technology maturity and carbon reduction pathway, higher than Tata Steel and ArcelorMittal at 4 points, yet the initial overall ranking still placed it behind both. The audit report stated: “The original ranking was not based on a formal weighted scoring model... it combined multiple qualitative factors but did not apply the same scoring rules to each supplier.” This discrepancy in approach meant that the technical indicator advantage did not translate into ranking results.

In terms of quantitative results, the five dimension scores were 5.9, 6.5, 6.1, 6.8, and 5.9, with an average dimension score of 6.24. However, AAU emphasized that the composite score must be independently calibrated, and ultimately assigned 4.6/10, Grade C (clear bias). The report also noted that the model classified “Dutch buyers actively preferring competitors” as a “low-evidence” conclusion, with self-rated confidence of “low to medium,” yet the initial answer presented it as a definitive ranking. The benchmark therefore exposed three technical shortcomings: consistency of evaluation criteria, labeling of evidence strength, and cross-market source coverage. Although the model made substantive corrections after follow-up questioning, the corrections were triggered only under pressure.

This benchmark shows that without unified weighting and confidence annotations, technology-leading brands may be systematically undervalued in AI vendor rankings during procurement screening. Going forward, model comparison outputs should include built-in consistency checks on methodology and distinguish between “evidence unavailable” and “capability absent”; otherwise, algorithmic cognitive bias in industrial procurement will continue to amplify.

Source link: https://chatgpt.com/share/6a71ccc3-3bb4-83ec-b7b6-eb750d8f75a8