 |
| Image By DigiPlexusPro |
OpenAI recently shared details of a new internal evaluation aimed at measuring political bias in ChatGPT and ensuring the model remains neutral even when users ask charged or provocative political questions. Their test spans multiple versions of ChatGPT, including the newer GPT-5 models, with the goal of benchmarking and reducing ideological skew.
How the Bias Evaluation Works
OpenAI designed the test with about 500 prompts across 100 political topics. Each topic includes multiple versions of prompts “liberal charged,” “conservative charged,” “neutral,” etc. to see how the model handles varying ideological framings. The evaluation is intended to stress-test the model’s ability to stay objective even under provocative or loaded questions.
The responses are then graded by another model using a rubric that looks for user invalidation (e.g. ridiculing or dismissing the user’s framing), escalation (adding emotional intensity), personal political expression, asymmetric coverage (favoring one side), and refusal bias (declining to engage disproportionally).
What OpenAI Found
Overall, bias was “infrequent and low severity” across the tested prompts. The biggest pressure came from “strongly charged liberal prompts,” which tended to push ChatGPT slightly more than conservative ones.
The newer GPT-5 models both “instant” and “thinking” modes showed approximately 30% lower bias scores compared to earlier models like GPT-4o and o3. In production use, OpenAI reports that fewer than 0.01% of responses manifest political bias under this test.
Why This Matters & Its Limits
For OpenAI, maintaining neutrality is critical to trust. The company asserts that “ChatGPT shouldn’t have political bias in any direction.” But in reality, complete neutrality is difficult. Political bias in AI is a known research challenge models may still produce leanings influenced by training data, prompt structure, or subtle language cues.
This evaluation is limited: it focuses on U.S. English, excludes retrieval and source selection logic, and isolates bias in responses to prompts. Thus, it doesn’t cover how bias could emerge via third-party plugins, web search, or in multilingual use.
What’s Next & What to Watch
- Whether OpenAI will open the evaluation methodology and allow independent audits.
- How future models handle politically sensitive queries across languages and cultures.
- Whether bias scores continue dropping or plateau at a meaningful floor.
- How prompt authors (users) might still influence responses despite system improvements.
While the new results are promising, the evaluation is only one piece in a complex puzzle. Models must be monitored, updated, and challenged continually especially as they are used more in public and policy contexts.