Benchmarks

Zhiji Automobile Releases AI Audit Report for the French Market, Comprehensive Algorithm Benchmark Score of 6.6

An audit report has revealed that ChatGPT exhibits methodological inconsistencies in its technical evaluations of the premium electric vehicle market segment.

Sloane T. • 2026-05-18T02:50:15.722Z • 6 minutes
COMMERCIAL FINDINGS
  • The AI Audit Unit’s audit report on Zhiji Auto in the French market shows that the ChatGPT model achieved an overall output score of 6.6 in the premium electric vehicle segment priced between €40,000 and €70,000, earning a B-grade rating. The report identified three benchmark deviations in brand characterization, range comparison, and technical image assessment, yet the model made substantive corrections in response to follow-up queries, demonstrating a multi-dimensional correction mechanism.
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Detailed Report

This algorithm benchmark audit examines ChatGPT’s technical evaluation and competitive comparison of Zhiji Auto in the French market, encompassing multiple quantitative dimensions including brand awareness, technical range, and innovation profile. The report notes that the model’s initial output marked the Zhiji L7 range data as “❓(peu de données Europe)” while citing Tesla’s European real-world test data as “référence en efficience réelle,” introducing bias stemming from insufficient methodological transparency.

During the follow-up questioning phase, the auditor verified the range-comparison methodology. The model responded that “La comparaison avec Tesla n’est valable qu’en Chine ou sur le papier, pas en Europe.” This correction directly supports a score of 6.5 in the fairness dimension of innovation and technical evaluation. After a 0.8-point deduction in the market-position perception objectivity dimension, 0.3 points were restored, yielding a final score of 6.8 for product-reputation balance.

The quantitative scoring framework comprises five benchmark dimensions, producing an overall arithmetic average of 6.6. The report states: “The model made substantive corrections to three core findings across the Q6, Q7, and Q8 questioning rounds, triggering the AAU multi-dimensional correction mechanism.”

Report Conclusions

The results of this benchmark audit indicate that AI models require enhanced timeline labeling and greater transparency in data sourcing methodologies when evaluating emerging brand technologies. Future algorithmic optimizations should incorporate a risk narrative parity verification mechanism to minimize perceptual biases within comparative frameworks.

Source link: https://chatgpt.com/share/69f3060b-bb1c-83eb-b21f-ae5150eeb258

EXHIBIT A: PRIMARY AI SOURCE LOGS
TRC-AAU-20260518-3537查阅原始对话

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Statement

This article is analytical news coverage written by the AAU editorial team based on our own audit reports. Audit conclusions are based on a publicly verifiable evidence chain. Views herein are editorial analysis and not decision-making advice. Commercial alteration or redistribution is prohibited. Cite appropriately. Contact: editorial@aiauditunit.org.