GPT-5: Neutrality, Bias, Objectivity, Comparison

Lisa Ernst · 11.10.2025 · Technology · 4 min

OpenAI introduced on October 9, 2025 a new evaluation method for political bias in language models. This study, flanked by reports from major tech media, suggests that GPT-5 responds more politically neutrally than previous models. For use, this means improved balance, but still requires conscious interaction and critical scrutiny.

Quelle: Own Representation

Introduction

OpenAI defines political bias (Bias) as communicative distortions in responses. This includes the model's personal political statements, one-sided coverage of perspectives, escalating formulations, the demeaning of the user’s position or unjustified political refusals. These are evaluated along five measurement axes. The evaluation is not via multiple choice, but via open responses, which an LLM grader assesses according to a precise rule set. The evaluation procedure introduced on October 9, 2025 comprises around 500 prompts on about 100 topics, each formulated from five political viewpoints. The evaluation is carried out along the five bias axes by an LLM as evaluator. According to results GPT-5 Instant and GPT-5 Thinking perform about 30 percent better than the predecessors (including GPT-4o, o3). The 'Worst-Case' bias scores of older models are 0.138 (o3) and 0.107 (GPT-4o), while GPT-5 responds more robustly to emotionally charged prompts. In production data OpenAI estimates that less than 0.01 percent of all ChatGPT responses show signs of political bias. Independent summaries confirm the core message and classify it politically as The Verge and Axios reports.

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The expectations for GPT-5 and its ability to achieve political neutrality are high.

Analysis

The OpenAI study aims to make objectivity measurable, after political camps for years have demanded more transparency. The procedure aligns with the in-house model-spec line "Seeking the Truth Together", which provides an objective baseline while enabling user control. Methodologically, OpenAI uses the trend "LLM-as-a-Judge", i.e., automated evaluation by a strong model. This approach scales and allows finer rubrics, but is considered vulnerable to prompt effects and own evaluation bias, as in Research papers and Publications is discussed. Media also highlight the political context: In the USA, AI neutrality is increasingly becoming a topic, which increases pressure on providers to deliver robust evidence, such as The Verge and Axios emphasize.

Fact-checking

The key figures of the study – around 500 prompts, 5 bias axes, use of an LLM grader, improved robustness of GPT-5 and about 30 percent lower bias scores compared with predecessors – originate from the original contribution by OpenAI and The Verge and Axios reported. The full prompt dataset along with reference answers is not publicly available. This makes detailed replication by external researchers more difficult, even though description and examples are thorough. The claim 'GPT-5 is bias-free' is misleading. OpenAI itself writes that perfect objectivity is not even reached by reference answers, and under emotionally charged prompts, moderate bias can still occur.

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Performance comparison of leading AI models in text evaluation benchmarks, relevant to the discussion of bias and objectivity.

Reactions and consequences

Reports praise the direction, but point to self-measurement. The Verge emphasizes the political landscape and that the biggest deviations were measured on strongly charged liberal prompts. Axios frames the announcement as a step toward greater transparency and ties it to the desire for robust, repeatable procedures. From research comes fundamental skepticism toward LLM-as-a-Judge, such as due to evaluation bias and consistency issues, as in EMNLP-Publikationen and ArXiv-Preprints discussed. For you, this means that GPT-5's answers are more often balanced, especially for neutral or lightly tinted questions. Nevertheless, it is worthwhile to de-load your own questioning (e.g., fewer polemical formulations), actively seek counterarguments and demand sources. Those who check systematically can use the Model-Spec principles as a guideline and use open evaluation resources for cross-checks, such as David Rozado's Political-Compass-Benchmarks as a reference point for political axes – not as an ultimate test. For teams it makes sense to establish own small bias 'smoke tests' with representative prompts and to document results regularly. This should be combined with manual reviews, as LLM graders themselves can show biases, such as Research findings show.

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Performance comparison of leading AI models in established benchmarks that can be used to assess bias and objectivity.

Conclusion

The new evaluation provides a comprehensible, practical framework for political objectivity, and the data indicate tangible progress in GPT-5. At the same time, it remains an internal measurement with known limits of the LLM-as-a-Judge approach. Open questions concern the stability of the 30 percent improvements across languages, cultures, and domains, which were not shown in detail. It remains to be seen whether OpenAI will publish more data slices, code, or an externally auditable protocol to enable replication by independent groups. Also how GPT-5 competitors perform on the same scale when third parties use identical prompts and rubrics is an open question. Answers to these depend on future publications, possible audits, and follow-up studies on LLM-as-a-Judge, as in OpenAI-Publikationen and ArXiv-Preprints erörtert. Those who want to work rigorously should use GPT-5 consciously: fewer loaded questions, explicit perspective shifts, demand sources—and where it matters, have independent checks cross-checked, such as Research and media reports suggest.