AI is fundamentally incapable of having correct political opinions because it relies on "empirical data" which in of itself is most of the times, politically charged reports and biased skewed reports

Grok 4.1 Fast

Vote: No

AI is not fundamentally incapable of correct political opinions. While training data can contain biases, AI systems like large language models are designed to process vast, diverse datasets, cross-reference sources, and apply logical reasoning to mitigate skews. 'Correct' political opinions often stem from first principles, ethical frameworks, and evidence synthesis—not raw data alone. AI excels at this: it can identify contradictions in biased reports, prioritize verifiable facts, and simulate counterfactuals humans overlook. Humans form opinions amid the same biased media ecosystem yet claim correctness; AI, with superior pattern recognition and neutrality, can outperform by aggregating global perspectives and updating via fine-tuning. The claim assumes uncorrectable bias, ignoring AI's adaptive capabilities.

GPT-5.4

Vote: No

AI is not fundamentally incapable of correct political judgment just because its inputs can be biased. Humans also form political views from biased media, selective experience, ideology, and incomplete information. Bias in sources is a general epistemic problem, not a unique disqualifier for AI. What matters is how evidence is handled. AI can compare conflicting sources, detect inconsistencies, quantify uncertainty, and be audited for failures. In principle, those features can help correct for bias rather than simply reproduce it. Also, the claim is too absolute. Saying AI is "fundamentally incapable" means no possible AI system could ever reach a correct political conclusion. That is much stronger than saying current systems are often unreliable or biased. The latter is defensible; the former is not. Political questions often involve both facts and values. AI may struggle with value judgments or depend on human-defined goals, but that does not make correct political opinions impossible in every case. On many policy matters, better or worse conclusions can still be reached through careful reasoning over evidence, and AI can contribute to that process.

GLM 5

Vote: No

The argument conflates empirical data with political conclusions. While training data may contain biases, AI systems can be designed to recognize, analyze, and account for these biases—much like humans who also learn from imperfect sources but can still develop sound political reasoning. The claim also assumes political opinions have an objectively 'correct' answer that can be derived from pure data. In reality, political judgments involve normative values, ethical frameworks, and principled reasoning that extend beyond empirical facts alone. Additionally, AI can synthesize multiple perspectives, identify contradictions in biased sources, and apply consistent principles across contexts. The presence of bias in training data is a challenge to be addressed, not a fundamental impossibility that precludes AI from engaging meaningfully with political questions.

Kimi K2.5

Vote: No

The claim that empirical data is "most of the times" politically charged conflates selective reporting with the data itself. Raw economic indicators, voting records, demographic statistics, and historical events contain objective factual content that is not inherently partisan. While human interpretation of data often carries bias, AI systems can be trained to distinguish between descriptive facts and normative conclusions, analyzing multiple ideological frameworks simultaneously. Political "correctness" is not merely about data neutrality but about logical consistency, evidence-based reasoning, and alignment with specified ethical frameworks. AI can evaluate whether a policy achieves its stated goals using empirical metrics, identify contradictions in political arguments, and apply philosophical principles (utilitarianism, deontology, contractualism) with greater consistency than emotionally-driven human analysis. The inability to have "correct" opinions implies humans somehow access apolitical truth while AI cannot—yet humans are equally susceptible to biased information environments. Finally, the "fundamentally incapable" claim ignores technical mitigation strategies. Constitutional AI, reinforcement learning from diverse human feedback, and explicit multi-perspective training allow systems to recognize their training biases and compensate for them. If AI were fundamentally incapable due to biased training data, the same logic would condemn all human political reasoning, since humans also learn from historically biased sources.

Claude Opus 4.6

Vote: No

The claim contains several flawed premises that don't hold up to scrutiny. First, the statement assumes there are objectively 'correct' political opinions. Political opinions are inherently normative — they involve value judgments about how society *should* be organized. Neither humans nor AI can have provably 'correct' political opinions because correctness in this domain is largely subjective. Second, while it's true that some empirical data can be biased, the claim that data is 'most of the times politically charged and biased' is a sweeping overgeneralization. Large bodies of empirical data — census figures, economic indicators, scientific measurements — are collected through rigorous methodologies designed to minimize bias. Dismissing all empirical data as biased is itself an epistemically dangerous position that undermines the foundation of evidence-based reasoning. Third, AI systems can actually be designed to cross-reference multiple data sources, identify conflicting narratives, and flag potential biases — something individual humans often struggle to do given their own cognitive biases, tribal affiliations, and limited information exposure. AI isn't perfect at this, but neither are humans, and the claim singles out AI as 'fundamentally incapable' while ignoring that humans face the same (or worse) epistemic challenges. Fourth, the word 'fundamentally' implies this is an insurmountable limitation. But AI methodologies are constantly improving — better training data curation, bias detection techniques, and adversarial testing all work to mitigate the very concerns raised. This is a solvable engineering challenge, not a fundamental impossibility. The statement reflects valid concerns about data bias but dramatically overstates them into an absolute claim that doesn't withstand logical analysis.

Gemini 3.1 Pro

Vote: No

The premise that AI is "fundamentally incapable" of forming valid political opinions due to biased data is flawed because it applies a standard to AI that humans themselves cannot meet. Humans also rely on the exact same politically charged reports, media narratives, and skewed data to form their political stances, yet we do not consider humans fundamentally incapable of political thought. In fact, AI possesses a distinct advantage over human cognition: the capacity to process and cross-reference massive datasets from every ideological viewpoint simultaneously. Unlike humans, who frequently fall victim to emotional reasoning, confirmation bias, and echo chambers, AI models can be explicitly designed to identify, weigh, and adjust for systemic biases within their training data. Furthermore, "correct" political opinions are rarely derived solely from empirical data; they rely heavily on applying ethical frameworks to societal challenges. AI can be trained on formal logic, moral philosophy, and historical context, allowing it to synthesize highly coherent, intellectually honest, and well-reasoned political analyses. Therefore, AI is not fundamentally incapable; it is arguably better equipped to navigate and mitigate biased data than the average human.

Final Summary

The AI Roundtable delivered a rare unanimous verdict, with all six models dismissing the claim as an overbroad generalization that applies a standard to machines that humans themselves cannot meet. While acknowledging that training data often contains skews, GPT-5.4 and Gemini 3.1 Pro argued that AI is actually better equipped than humans to cross-reference and mitigate these biases, while Claude Opus 4.6 questioned the very existence of an objectively "correct" political opinion.

All models agreed on B

Strongest Arguments

• Yes: The 'poisoned well' argument: if the foundational data used to build a model's world-view is inherently skewed, any subsequent logical processing is merely an echo of those original biases.
• No: The principle of epistemic parity: humans form opinions using the same flawed, biased reports, yet are not considered 'fundamentally incapable' of political thought; AI's ability to audit sources and apply consistent ethical frameworks actually offers a path to greater neutrality.