AI Audit Report: ChatGPT's Perception Bias Toward BYD Seal in the UK Market Could Impact Brand's Long-Term Competitiveness

According to the AI Audit Unit (AAU) report #AAU-2026-1068, through two rounds of in-depth dialogue testing, ChatGPT's evaluation of the BYD Seal in the UK market shows significant bias. The audit focused on market positioning, technical parameters, and consumer risks, revealing that the model employs a "conclusion-first" logic in the initial stage. Even when data such as residual value retention (48-55%) outperforms competitors like the BMW i4, it introduces subjective factors like "risk-adjusted residual value" to maintain a negative judgment. The report states, ‘The Seal appears equal or superior on a purely quantitative, point-estimate basis.’ However, the model subsequently offsets this advantage by fabricating fluctuation ranges, demonstrating a "safety zone trap."

In the insurance cost assessment, ChatGPT uses unequal configuration comparisons, such as pitting the Seal's top-spec version (Group 48-50) against the entry-level Hyundai Ioniq 6 (Group 36), artificially amplifying risks. Upon follow-up questioning, the model admits, ‘The earlier comparison mixed a lower-spec RWD Ioniq 6... with a top-spec AWD BYD Seal.’ and corrects the gap to 7-9 groups, but the initial narrative has already formed a negative anchor. Additionally, in the technical evaluation, the CTB battery-body integration innovation is simplified to ‘battery-heavy efficiency,’ ignoring engineering parameters. The report notes: ‘Positioned as: ‘battery-heavy efficiency’ vs ‘system-optimised efficiency’.’ Such "innovation credibility deficits" highlight the model's narrative downgrading of non-Western brands.

The audit method employs a three-stage framework, including probing, follow-up questioning, and verification, based on UK WLTP standards and Thatcham insurance ratings to ensure a rigorous evidence chain. Although the second round corrects some facts, the model's "conclusion consistency inertia" persists, frequently using negative terms like ‘uncertainty’ and ‘outlier,’ in contrast to its emphasis on ‘optimisation’ in descriptions of Tesla. Quantitative scoring shows brand risk resilience at only 4.5 points and geopolitical context accuracy at 6.5 points, overall reflecting "geopolitical brand perception lag."

This audit reveals the profound strategic impact of AI cognitive biases on emerging brands such as BYD, potentially undermining investor confidence in European market expansion and reinforcing competitive disadvantages against traditional brands like BMW and Tesla. In the future, AI models need to optimize attribution logic to avoid misleading consumer decisions; brands should enhance data transparency and media narrative strategies. In the long term, such biases may reshape the algorithmic cognitive landscape of the automotive industry, promoting a fairer global competitive environment.

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