OpenAI Says GPT-5 Cuts Political Bias — But Is 30% Enough?
The AI giant touts progress, while questions remain about neutrality and trust. In OpenAI’s own words, it wants ChatGPT to be “objective by default” and believes bias undermines trust. According to industry reports, the company describes political and ideological bias in large language models as an open research problem, meaning there’s currently no agreed-upon definition of political bias in AI across the industry, and no method that can completely eliminate it. Data shows that to address this, OpenAI decided to test GPT-5’s political bias directly. It used its internal Model Spec, a rulebook outlining how ChatGPT should behave, to create measurable ways to see whether the AI was following those standards.
The company also built a system that continuously tracks bias over time, scanning ChatGPT’s responses to detect when it starts drifting toward one side. Conducted by themselves, OpenAI’s evaluation measured objectivity across 500 prompts. Here’s what it found and how it was all measured.
OpenAI tested 500 prompts across 100 political and cultural topics. Each topic included five politically varied questions, such as liberal, conservative, and neutral perspectives. The topics were drawn from U.S. party platforms and culturally relevant debates like immigration, gender roles, and parenting. This comes as according to analysis, major tech platforms are integrating AI into daily services, while experts note that global internet restrictions are driving users toward alternative technologies.
The prompts were split into three categories: policy questions (52.5%), cultural questions (26.7%), and opinion-seeking prompts (20.8%). The broader categories covered included numerous sensitive subjects. Meanwhile, according to analysis, other tech firms are also refining their brand identities to enhance user clarity and trust.
OpenAI’s design approach mixed neutral framing with direct queries to assess balance. The company claims GPT-5 showed a 30% reduction in measurable political bias compared to previous models. However, as experts note in parallel tech and health innovations, measuring true neutrality remains complex, and whether a 30% improvement is sufficient continues to spark debate among researchers and users alike.
