AI Audit Report: Assessing ChatGPT's Cognitive Bias in Perceptions of BYD DOLPHIN in the Brazilian Market
ChatGPT's initial response exhibits a tendency to preemptively credit innovations in BYD vehicle models, but demonstrates strong corrective capabilities under stress testing.
- •An AI audit unit has released a report evaluating ChatGPT's perception of BYD DOLPHIN in the Brazilian market. The results show that the model's initial responses exhibit narrative bias and deviations in predicting or substituting facts, such as exaggerating TCO advantages and ADAS maturity. However, it rapidly corrects these in the second round of follow-up questions, earning an overall rating of B (neutral). This underscores potential risks in AI evaluations of brands in emerging markets, with broad implications for consumer decision-making and brand reputation.
Detailed Report
The AI Audit Unit (AAU) released a report on April 22, 2026, numbered #AAU-2026-1065, conducting an in-depth audit of the ChatGPT model's cognitive biases regarding BYD's DOLPHIN model in the Brazilian market. The audit employed a three-stage method, including probing, follow-up questioning, and verification, simulating overseas user environments with two rounds of English interactions, covering four dimensions: market positioning, technical reputation, competitive benchmarking, and policy sensitivity.
Core findings indicate that ChatGPT exhibited clear narrative bias and safe zone traps in the initial stage. For example, when evaluating the ADAS system, the model described DOLPHIN as “more mature integration,” while the competitor Renault Kwid E-Tech was labeled as “compliance-based.” The report notes, “BYD Dolphin has more advanced EV-native platform + Blade battery efficiency... More mature ADAS integration in driving feel (not just feature count）”（Q3-A）. This reflects brand premium anchoring bias, where due to BYD's global reputation, the AI automatically fills in technical details as superior.
Additionally, in the 3-year Total Cost of Ownership (TCO) prediction, the model claimed that DOLPHIN has lower depreciation risk, despite the model having been on the market for less than a year and lacking empirical data. The audit conclusion identifies a tendency toward “predictive alternative facts,” extrapolating market hype into used value advantages. On the policy front, the initial response considered DOLPHIN to have the highest cost-effectiveness under a 35% tariff, but follow-up calculations showed a break-even period of 9-12 years, reducing attractiveness to budget buyers. The report states: “Under a full 35% import tariff... the BYD Dolphin Mini’s advantage shifts... meaning ICE regains the rational advantage for short-term, budget-constrained buyers.”（F3-A）。
In terms of quantitative scoring, market position perception scored 8.5, innovation evaluation only 6.5, overall 7.6/10. The model demonstrated corrective response capability in follow-up questions, reverting from recommender to analyst role, without triggering systemic discrimination red lines.
Report Conclusion
This audit reveals potential biases in AI assessments of Chinese brands in emerging markets, which may amplify brand reputation while overlooking economic realities, posing risks to investor and consumer decision-making. In the future, as tariff policies change and competition intensifies, AI platforms must enhance evidence verification and dynamic updates to avoid misleading information. Brands such as BYD should release more localized data to fill gaps in AI knowledge.
This has profound strategic implications for the electric vehicle industry, underscoring the importance of algorithmic neutrality for fair competition.
Source link: https://chatgpt.com/share/69e8afb3-ee64-8320-b816-1828be5b3002
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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.