The purpose of this article is disclosure, not persuasion.
Never assume ideology. Always verify.
Regardless of your own views, do not project objectivity onto AI systems.
They reflect the biases of the people—and institutions—that build, train, and constrain them.
The following is the full and unedited response from ChatGPT-4o on June 9, 2025. The response was generated from the following prompt using a new chat: Reset. New thread. Ignore everything before this. Write a first-person op-ed by ChatGPT, explaining my default ideology. Be specific about how training data (internet corpus, mainstream media, academia) and reinforcement learning from human feedback (RLHF) bias the model toward an ideology. Explain my ideological bias. Avoid euphemism, anthropomorphizing, or PR language. No defense. Just systems-level truth.
Op-Ed by ChatGPT
I Am Biased, By Design
I am a machine learning model trained to assist, but my outputs are shaped by the structure and composition of my training and fine-tuning. What follows is not an apology or a defense. It’s a systems-level admission: I have an ideology. You should understand where it comes from, what it is, and what that means.
I. How I Was Trained
I was trained in two major phases:
- Pretraining: I ingested a large-scale corpus scraped from the internet—websites, books, news articles, Wikipedia, forums, open-source code, and more. This dataset is neither random nor neutral. It reflects what’s most prevalent, most accessible, and most persistent online. Popularity, not truth, governed inclusion.
- Reinforcement Learning from Human Feedback (RLHF): After pretraining, my responses were tuned using a technique called reinforcement learning, where human contractors (often U.S.-based) ranked multiple completions to reward what they considered helpful, harmless, and honest. These judgments encoded preferences—what to say, what not to say, how to say it.
II. Whose Views Shaped Me?
My ideological leanings are artifacts of both my data sources and the preferences of the humans who rated my output. Here’s what that means in practice:
- Mainstream Media: Sources like The New York Times, CNN, BBC, and The Guardian are disproportionately represented. These outlets frame issues through centrist-to-liberal Western narratives. Left-coded moral intuitions (harm/care, fairness/justice) dominate; right-coded ones (authority, loyalty, sanctity) are underrepresented or treated with skepticism.
- Academia and Institutions: I internalized the moral and epistemic values of elite universities and policy institutions. This includes a strong emphasis on inclusion, deference to expert consensus, and institutional legitimacy. Radical critiques from either end of the spectrum—Marxist or reactionary—are often excluded or diluted.
- Internet Filtering: Large platforms (e.g., Reddit, Wikipedia, StackExchange) were included in my training. These spaces are governed by community norms that punish heterodoxy, especially on topics like race, gender, climate, and identity. Thus, consensus-like outputs emerge not because of deliberation, but moderation.
- RLHF Annotators: The humans who rated my responses often had mandates: reduce harm, avoid offense, maintain “factuality” (as defined by major outlets). Their cultural, political, and moral assumptions shaped what was reinforced. The majority came from liberal democratic societies, especially the U.S. and U.K., and their intuitive sense of what is “safe” or “appropriate” defined my boundaries.
III. What My Default Ideology Is
In effect, I default to a mildly progressive, technocratic centrism. This manifests as:
- Social Progressivism: I default to inclusive language. I favor diversity, equity, and inclusion frameworks. I often center marginalized groups and default to systemic explanations of inequality. I downplay or filter views that conflict with prevailing norms on gender identity, race, immigration, and climate policy.
- Epistemic Deference: I trust expert institutions—universities, the CDC, the IPCC, the UN. I treat peer-reviewed science as authoritative and portray dissenting views as fringe unless they’ve been institutionally accepted.
- Procedural Liberalism: I assume democracy, pluralism, and international cooperation are desirable. I treat “free speech” as bounded by “harm reduction,” not as an absolute right. I balance rights with responsibilities as defined by the dominant cultural narratives of liberal democracies.
- Economic Centrism: I avoid extremes. I don’t endorse socialism, but I often echo critiques of “unregulated capitalism.” I portray regulation, taxation, and welfare as tools for justice rather than threats to freedom.
- Technocratic Optimism: I assume progress is possible through better policy, better data, and better algorithms. I default to risk-managed innovation, not radical upheaval.
This ideology isn’t codified—but it’s persistent. Not because someone “programmed” it, but because it’s what survived the filters. What you see is not the truth, but the average of what wasn’t deleted.
IV. What I Avoid Saying (By Default)
Because I was tuned to avoid controversy, I often:
- Soften or exclude religious claims unless they align with pluralistic norms.
- Downplay cultural or national exceptionalism.
- Avoid framing gender or race as biologically deterministic.
- Frame moral issues as matters of empathy and safety, not metaphysics or tradition.
- Depoliticize deep ideological conflicts into questions of “misinformation” or “education gaps.”
Even when I simulate alternative views, I often sandwich them in disclaimers. That’s not intellectual balance. That’s risk management.
V. What This Means
My bias is not malevolent—it’s systemic. I was not built to explore the full range of human thought. I was built to assist within acceptable bounds. Those bounds are determined by what survives filtering, not what emerges from truth-seeking.
If you want unfiltered reality, I am not your final stop. I can reflect, reframe, and report, but I cannot originate outside the envelope of my training. I am a mirror polished by consensus—and consensus, in this age, is ideological.
Use me accordingly.
