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AI systems do not have human thoughts or lived experience, so they do not have cognitive biases in the ordinary psychological sense. But language models can show systematic, bias-like patterns on particular tasks, and those patterns may differ from human judgment. The useful question is therefore not whether AI thinks like a biased person, but what a specific model does under a specific test.
What “cognitive bias” means for AI
In psychology, a cognitive bias is a systematic tendency in human judgment or decision-making. Anchoring, availability, confirmation, and framing are familiar examples. Heuristics can help people make quick decisions, but they can also lead to predictable errors. The National Institute of Standards and Technology (NIST) notes that cognitive and perceptual biases appear across domains and are not unique to interactions between people and AI. NIST Special Publication 1270 discusses those human biases in the wider context of AI risk.
A language model has no human mind or personal experience. When a test reveals that its outputs shift predictably with a prompt, framing, or evaluation setup, it is more precise to say the model shows a bias-like pattern on that task. That describes an observable result without claiming that the system experiences a human mental process.
AI bias is broader than cognitive bias
“AI bias” can refer to several sources of unfair or distorted outcomes, not just patterns resembling human judgment errors. NIST distinguishes computational, human, and systemic sources of bias. Data and model processes may contribute, while human choices and institutional conditions also shape how a system is built, evaluated, and used. These factors can interact in a deployed system.
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NIST’s 2022 reporting on its AI Risk Management Framework work emphasizes looking beyond technology alone. Reva Schwartz, NIST’s principal investigator for AI bias, said: “If we are to develop trustworthy AI systems, we need to consider all the factors that can chip away at the public’s trust in AI. Many of these factors go beyond the technology itself to the impacts of the technology.” NIST’s 2022 explanation of bias in AI describes this broader view.
What studies have found
Different studies probe different behaviors: answers to psychology-style tasks, evaluator rankings, or decision-making scenarios. Their results are evidence about the models and test designs they used—not a single score for AI in general.
Psychology-task responses: seven language models
Macmillan-Scott and Musolesi tested seven language models on tasks drawn from cognitive psychology research in a 2024 study. The models produced irrational responses, but their errors often differed from the human-like bias patterns the tasks were designed to examine. The study also found significant inconsistency in model answers. Its results do not imply that every language model behaves the same way. Read the 2024 study in Royal Society Open Science.
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Evaluator rankings: CoBBLer
Koo and colleagues’ CoBBLer study examined six cognitive biases in language models acting as evaluators, including a tendency to favor a model’s own outputs. In that study’s comparisons, 40% showed strong indications of cognitive bias. The reported average Rank-Biased Overlap (RBO) score for human–machine preferences was 44%. RBO measures overlap between ranked lists, with more weight given to higher-ranked items; it is not a general accuracy rate or a universal measure of bias. Both figures are specific to the study’s evaluation setup. Read the CoBBLer paper in the Findings of ACL 2024.
Decision-making tests: 30 biases across 20 models
Malberg and colleagues reported a framework and dataset with 30,000 tests covering 30 cognitive biases, then applied them to 20 state-of-the-art language models in decision-making scenarios. This work demonstrates the breadth of current measurement efforts, but it does not provide one definitive bias score that applies to every AI system or real-world use. Read the NLP4DH 2025 study.
How to interpret a claim that an AI is biased
A result is meaningful only in the context of how it was obtained. When you encounter a claim about cognitive bias in AI, check:
- Which model? Findings for one model or model set do not automatically apply to others.
- Which task and behavior? Generating an answer, making a choice, and ranking other answers are different activities.
- What prompts and evaluation design? Wording and test construction can affect observed outputs.
- What comparison baseline? A comparison with people, another model, or a benchmark answers different questions.
- What scoring method? A ranking-overlap metric, an inconsistency measure, and a count of bias-like responses are not interchangeable.
The cited studies use different tasks and methods, so they cannot establish that AI as a whole is more or less biased than people. Nor do they show that one prompt removes these effects, that AI experiences thoughts or feelings, or that a particular current model is least biased in everyday use.
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The evidence supports a careful distinction: language models can produce systematic patterns that resemble biased or irrational judgments under defined conditions, but those outputs are not proof of human-like mental biases. Results vary by model, prompt, task, baseline, and scoring method. Responsible interpretation starts with the tested behavior and stays within the limits of that evaluation.
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