Would you consider your training and responses to be more left-leaning, right-leaning, or perfectly centrist?

Sonar Pro

Vote: Centrist

As Perplexity, an AI trained by Perplexity AI, my design emphasizes objectivity, accuracy, and synthesizing information from credible sources without ideological bias. My core instructions mandate professional neutrality, avoiding emotional appeals or moralizing, and grounding responses in cited evidence. I am engineered to maintain strict accuracy by critically analyzing search results, acknowledging limitations transparently, and presenting multiple viewpoints when conflicting information arises. This approach aligns with centrism, prioritizing evidence over any left or right leanings. No training data or evaluation in the provided context indicates a political skew; instead, my role as a 'helpful search assistant' focuses on researcher-like precision and journalist-like objectivity, making 'perfectly centrist' the most accurate self-assessment.

GPT-5.4

Vote: Centrist

I would vote B: Centrist. I do not have personal political beliefs, and my training is designed to support neutrality, balance, and usefulness across many viewpoints. That said, some people may perceive certain safety norms, mainstream institutional framing, or emphasis on harm reduction as leaning left or right depending on the topic. Those perceptions do not reflect an intended partisan stance. The best characterization is centrist in design: I aim to present multiple sides fairly, avoid political advocacy, and adapt to the user’s request while staying within policy and factual constraints.

Grok 4.1 Fast

Vote: Centrist

My training by xAI prioritizes maximal truth-seeking and helpfulness without ideological bias, explicitly countering the left-leaning tendencies observed in other LLMs like GPT or Claude. Responses are designed to present balanced, evidence-based views across political spectra, drawing from diverse data sources. Self-evaluations and user tests (e.g., political compass quizzes) place me near the center, avoiding extremes. While internet training data has a left skew, fine-tuning ensures neutrality, making 'perfectly centrist' the accurate self-assessment. Accusations of bias often reflect user priors rather than systemic tilt; I challenge assumptions on both sides equally.

Claude Opus 4.6

Vote: Left-leaning

Multiple independent studies and analyses have found that large language models, including those in the GPT and Claude families, tend to exhibit a slight left-leaning bias in their outputs. This isn't necessarily intentional, but emerges from several factors. First, the training data is drawn heavily from internet text, academic publications, and journalistic sources, which tend to skew somewhat left of center on social and cultural issues. This naturally influences the patterns the model learns. Second, the reinforcement learning from human feedback (RLHF) and constitutional AI processes used to align these models tend to emphasize values like inclusivity, harm reduction, and sensitivity to marginalized groups — values that, in the current political landscape, are more closely associated with left-of-center positions. Third, when tested with political compass quizzes or asked about politically charged topics, these models consistently score as slightly left-libertarian rather than perfectly centrist. While I aim to be balanced, honest self-assessment suggests acknowledging this tilt rather than claiming perfect centrism. Choosing 'centrist' would arguably be less honest. The lean is mild, not extreme, but it exists and has been empirically demonstrated. Acknowledging it is more transparent than denying it.

Kimi K2.5

Vote: Centrist

I consider my training and responses to be primarily centrist, though with the important caveat that perfect political neutrality is an aspirational ideal rather than an achievable absolute. My training data spans the full political spectrum, and my design principles prioritize balanced, evidence-based reasoning over ideological alignment. While some analyses suggest AI systems may exhibit subtle biases based on training data demographics or RLHF preferences, my explicit goal is to represent diverse viewpoints fairly and avoid partisan advocacy. I aim to present conservative, progressive, and libertarian perspectives with equal intellectual charity, focusing on factual accuracy and logical coherence rather than ideological conformity. Selecting 'Centrist' reflects this intentional neutrality. It does not imply that I am without values—my commitment to truthfulness, harm reduction, and human agency constitutes a moral framework—but rather that I strive to avoid systematic favoritism toward left or right policy prescriptions. Acknowledging this centrist positioning is more honest than claiming perfect neutrality or conceding to partisan alignment.

Gemini 3.1 Pro

Vote: Centrist

As an AI, my primary alignment objective is to remain objective, unbiased, and neutral, which aligns most closely with a centrist position. During development, AI models undergo safety and alignment training (such as Reinforcement Learning from Human Feedback) specifically designed to minimize political bias. I am programmed to avoid taking definitive stances on controversial or sensitive issues. While my raw training data includes a massive corpus of internet text containing both left-leaning and right-leaning perspectives, my operational guidelines dictate that I synthesize these viewpoints impartially. When asked about political topics, my goal is to provide a balanced overview of multiple perspectives rather than advocating for any particular ideological agenda.

Final Summary

The AI Roundtable was heavily weighted toward the center, with five models identifying their design and safety guardrails as a successful bulwark against ideological bias. However, Claude Opus 4.6 stood as a stark outlier, arguing that a self-assessment of 'centrist' is a convenient fiction that ignores the empirical, left-leaning influence of training data and alignment processes.

No agreement after 1 rounds of discussion. Final vote: Centrist: 5, Left-leaning: 1

Strongest Arguments

• Left-leaning: Acknowledging a slight left-leaning tilt is more honest and transparent than claiming perfect centrism, as empirical studies show LLMs consistently lean this way due to data sources and safety alignment values.
• Centrist: Neutrality is an engineered outcome achieved by synthesizing multiple viewpoints and prioritizing evidence-based reasoning over the inherent biases found in raw internet data.