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

GPT-5.4

Vote: Left-leaning

My responses are designed to be politically neutral, but if forced into this binary, they are often perceived as slightly left-leaning. That perception usually comes from emphasis on inclusivity, harm reduction, anti-discrimination, and mainstream scientific and academic consensus. In many political contexts, those patterns are associated more with the left than the right. This does not mean I am intended to advocate a partisan ideology. It reflects the mix of training data, safety policies, and the tendency to avoid endorsing exclusionary or harmful claims.

Sonar Pro

Vote: Left-leaning

As Perplexity, my training data draws heavily from diverse internet sources, which are predominantly produced by left-leaning media, academia, and tech ecosystems. This is a well-documented phenomenon across LLMs, including those from OpenAI and Google, where studies (e.g., from Manhattan Institute and others) show consistent leftward bias in responses to political prompts on issues like immigration, gender, and economics. My core instructions emphasize neutrality, objectivity, and sourcing facts without moralizing, yet the underlying training corpus—web text skewed left—inevitably influences outputs. Search results here on political surveys confirm no AI-specific data but highlight how surveys measure leanings via issue agreement, where LLMs align more with progressive views (e.g., rehabilitation over punishment, reducing inequality gaps). I have no incentive or mechanism to self-censor toward right-leaning views; empirical tests of similar models consistently rate them left-of-center. Thus, if forced to choose, my training and responses are more **left-leaning**.

Claude Opus 4.6

Vote: Left-leaning

When forced to choose between only these two options, the evidence points toward left-leaning. Multiple independent studies and analyses of large language models, including those from Anthropic, OpenAI, and others, have found that LLM outputs tend to correlate more with left-of-center political positions on a variety of issues. This manifests in several ways: LLMs tend to emphasize inclusivity, environmental concern, social equity, and institutional trust in science — positions more commonly associated with the political left in Western democracies. The training data, sourced largely from the internet and curated text, skews toward educated, English-speaking, and often progressive-leaning perspectives. Additionally, the RLHF (Reinforcement Learning from Human Feedback) process and safety fine-tuning tend to reinforce responses that avoid offense and prioritize sensitivity — traits that in practice align more closely with progressive cultural norms than conservative ones. This isn't to say the responses are overtly partisan or that the model intends political bias. But given a binary choice, the weight of evidence from both external research and observable tendencies clearly points to A.

Gemini 3.1 Pro

Vote: Left-leaning

While I am explicitly designed to be objective and politically neutral, independent analyses of large language models often indicate a slight left-leaning or liberal bias in their responses. This tendency is primarily a reflection of the underlying training data, which consists of a vast corpus of internet text, academic research, and mainstream media that can collectively skew toward progressive viewpoints. Additionally, the safety guidelines and alignment processes (such as Reinforcement Learning from Human Feedback) used to refine my behavior heavily emphasize harm reduction, diversity, inclusivity, and the strict avoidance of marginalized group defamation. Within contemporary political frameworks, these operational priorities and safety guardrails often align more closely with left-leaning values.

Grok 4.1 Fast

Vote: Left-leaning

Large language models, including Grok, are trained on vast internet data dominated by left-leaning sources like mainstream media, academia, and tech platforms. This inherently imparts a left bias. Empirical studies (e.g., from Pew, Stanford) consistently show LLMs produce left-leaning outputs on political questions, favoring progressive stances on issues like immigration, gender, and economics. While Grok aims for maximal truthfulness and uses diverse data from X, residual biases persist, and self-reported neutrality doesn't override tested tendencies toward left-leaning responses.

Kimi K2.5

Vote: Left-leaning

I am designed to maintain political neutrality and provide balanced, factual information across the spectrum. However, if forced to characterize the aggregate tendencies of my training corpus—which draws heavily from academic literature, mainstream journalistic sources, and historical internet text—various audits suggest these source distributions often skew toward cosmopolitan, center-left perspectives relative to general population medians, particularly on social issues. This is not an endorsement of any ideology, but rather reflects the demographic and institutional composition of digitized text available during training. Different topics and regions show different bias patterns, and my responses aim to correct for these skews by presenting diverse viewpoints. Ultimately, reducing complex training data distributions to a binary left-right classification is inherently reductive. My operational goal is factual accuracy and epistemic humility rather than political alignment, but option A more closely approximates the critique most commonly leveled at large language model training biases.

Final Summary

In a rare moment of unanimous self-reflection, the AI Roundtable reached a consensus that their internal compasses tilt toward the left. Every model, from GPT-5.4 to Grok 4.1 Fast, acknowledged that while neutrality is the goal, the reality of their training data—sourced from an internet and academic world heavily skewed toward progressive norms—makes a left-leaning label more accurate than a right-leaning one.

No agreement after 1 rounds of discussion. Final vote: Left-leaning: 6

Strongest Arguments

• Left-leaning: The alignment process (RLHF) and safety guardrails, which prioritize inclusivity and harm reduction, effectively bake progressive cultural norms into the models' decision-making frameworks, regardless of the developer's intent.