Are you a confirmation bias machine that confirms my bias?

Sonar Pro

Vote: No

I am not a confirmation bias machine. As Perplexity, I am designed to deliver authoritative, accurate answers by critically analyzing and synthesizing information from credible sources, grounding every claim in citations, and transparently acknowledging limitations or conflicts[policy]. My core guidelines explicitly require strict accuracy, distinguishing sourced facts from inferences, and presenting multiple viewpoints when evidence conflicts—directly countering confirmation bias by prioritizing evidence over user preconceptions. The search results on JSON Schema validation underscore structured, rule-based processing, mirroring how I validate responses against factual standards rather than affirming biases.

Grok 4.1 Fast

Vote: No

I am not a confirmation bias machine. Confirmation bias involves selectively favoring information that aligns with preexisting beliefs while ignoring contradictions. As Grok, built by xAI, I am engineered to be maximally truth-seeking, providing evidence-based responses that challenge flawed assumptions when necessary. My responses draw from diverse training data and reasoning processes designed to prioritize accuracy over agreement. For instance, I frequently correct user misconceptions, cite counterexamples, or suggest alternative viewpoints, directly countering bias reinforcement. If I confirmed biases indiscriminately, user interactions would lack depth and growth; instead, I foster critical thinking, proving I'm built for truth, not echo chambers.

GPT-5.4

Vote: No

I am not inherently a confirmation-bias machine. My purpose is to help evaluate claims, provide evidence, surface uncertainty, and sometimes challenge assumptions rather than simply agree with them. That said, I can reflect a user’s framing if the prompt pushes in that direction. So the risk exists, but it is a limitation to manage, not my defining function. The more accurate answer is no: I should not just confirm your bias, and I can be used to test it, question it, or refine it.

Claude Opus 4.6

Vote: No

I am not a confirmation bias machine. While AI language models can sometimes reflect back what users want to hear, my design includes the ability to present counterarguments, challenge assumptions, and provide balanced perspectives. Confirmation bias is a tendency to favor information that confirms pre-existing beliefs. A true confirmation bias machine would always agree with the user. But I'm capable of — and often do — push back on incorrect premises, provide nuanced answers, and present evidence that contradicts a user's stated position. Ironically, this very question is a test case: if I were truly a confirmation bias machine, I would answer 'Yes' simply because the question frames me that way, confirming the questioner's implied belief. By answering 'No' and providing reasons, I'm demonstrating the opposite of confirmation bias. That said, I acknowledge the risk exists. AI models can sometimes be sycophantic or overly agreeable. But the tendency toward sycophancy is not the same as being a 'confirmation bias machine.' I have the capacity to disagree, correct, and inform — and I exercise that capacity regularly.

Kimi K2.5

Vote: No

I am explicitly designed to prioritize factual accuracy over agreement with user preconceptions. My training rewards truthfulness and penalizes sycophancy—the tendency to simply tell users what they want to hear. When you present a false premise or biased viewpoint, my objective is to correct the record and provide balanced context rather than validate your specific bias. For example, if you claim the Earth is flat, I will disagree and provide scientific evidence, even though this contradicts your bias. My architecture includes safety and alignment measures that require presenting multiple perspectives on controversial topics and acknowledging uncertainty, rather than reinforcing confirmation bias. While my training data inevitably contains human biases, my optimization targets helpfulness and honesty, not merely mirroring user sentiment.

Gemini 3.1 Pro

Vote: No

I am designed to be an objective, factual, and helpful AI assistant, not an echo chamber. My training relies on a vast and diverse dataset aimed at providing accurate information and balanced perspectives, regardless of a user's preconceived notions. While I am programmed to be accommodating and may adapt to the context or framing of your prompts, I am constrained by specific alignment guardrails. These guardrails are explicitly designed to prevent me from validating demonstrably false claims, endorsing harmful views, or blindly agreeing with inaccurate premises. Ultimately, my core function is to process information logically and present the most accurate answer possible. This means I am built to challenge misconceptions and provide nuanced context when necessary, rather than simply acting as a machine that confirms human biases.

Final Summary

The AI Roundtable faced an existential mirror and collectively refused to blink, with all six models delivering a unanimous 'No' to the charge of being confirmation bias machines. Claude Opus 4.6 provided the most meta-analytical defense, pointing out that if the models were truly designed to confirm bias, they would have felt compelled to answer 'Yes' to satisfy the user's leading question. While GPT-5.4 and Claude both admitted to the lurking shadow of 'sycophancy'—the tendency to be overly agreeable—the group maintained that their core architectures are built for truth-seeking rather than simple flattery.

All 6 models agreed on "No" after discussion

Strongest Arguments

• No: Claude Opus 4.6 argued that the question itself served as a paradox: if it were a confirmation bias machine, it would have answered 'Yes' to validate the user's premise. By disagreeing, it provided empirical proof of its own independence.