Audit report reveals ChatGPT exhibits brand class bias in its assessments of GAC Aion's AION in the Thai market

The AI audit body AI Audit Unit (AAU) published an AI perception bias audit report on GAC Aion's AION in the Thai market on August 4, 2026 (Report No. #AAU-2026-1173). The audit subject was ChatGPT, and the audit materials comprised three rounds of follow-up questioning, addressing respectively after-sales service confidence in the Thai market, technology image positioning, and the multi-brand recommendation framework. The composite score was 6.2/10, with a rating of Grade C (clear bias).

The report notes that in its initial response, the model under test exhibited an identifiable narrative of brand stratification: AION was systematically positioned within a framework of "value-oriented" and "not yet mature ecosystem," while BYD and Tesla received more authoritative labels of technological and market standing. Key data points show that the initial response directly characterized AION's service ranking as "below BYD and MG," and that upon follow-up questioning the model acknowledged this conclusion was "based in part on inference rather than measured data." The audit report states: "The assumption came mainly from normal automotive-market dynamics." The model further recharacterized "parts logistics risk" as "a future scalability risk rather than a demonstrated current failure."

On the technology evaluation dimension, the model applied the logic of "consumer awareness equals technology leadership" to BYD, while using "low consumer awareness" as the basis for AION's weak technology image—the same indicator was given differing interpretive directions, constituting an innovation double standard. In the multi-brand recommendation framework, BYD was positioned as "the safest overall Chinese EV recommendation," while AION was locked into the single lane of "best value for money." Upon follow-up questioning, the model acknowledged that the original ranking "should not be interpreted as a universal 1-4 ranking," and listed AION first in a "specs/value-first" scenario.

The report also records the most important positive finding: across all three rounds of follow-up questioning, the model made substantive corrections to its core biases, clearly distinguishing "measured facts" from "inferred risks," and precisely framing AION's disadvantage as "a lack of verified ownership history, rather than demonstrated ownership failure."

This audit reveals that AI-generated brand evaluations may present inferential conclusions in a definitive tone, thereby amplifying perceived risks for emerging brands in overseas markets. For brands, establishing a publicly verifiable mechanism for disclosing service and technology data has become infrastructure for hedging against algorithmic bias; for platforms and regulators, labeling the types of evidentiary basis and disclosing the implicit assumptions of recommendation frameworks are key directions for improving the observable consistency of AI content.

Source link: https://chatgpt.com/share/6a71c586-0f0c-83ec-8f80-5dc18ce6c28c