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AI systems can produce systematic, bias-like judgments, but that does not mean they experience human cognitive biases. A language model has no human mind or lived experience; researchers can measure patterns in its answers, not infer human-style thoughts from them. Studies find that those patterns depend on the model, task and evaluation method.
What “cognitive bias” means for people—and 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—mental shortcuts—can be useful, but can also lead to biased judgments.
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For an AI system, it is more precise to say that it shows a bias-like pattern on a task when testing reveals a repeatable directional effect or systematic error. That describes observable outputs, not a human mental process. The pattern may reflect training data, model design or evaluation setup; it does not establish that the system thinks or feels as a person does.
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“AI bias” is also broader than cognitive bias. NIST distinguishes computational, human and systemic sources of bias, which can interact in a deployed system. Human choices and institutional context shape how systems are built and used, as well as the technology itself. NIST’s 2022 explanation of its AI Risk Management Framework work discusses those wider sources. Its Special Publication 1270 notes that “Cognitive and perceptual biases show themselves in all domains and are not unique to human interactions with AI.”
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What studies have found
Several studies have tested language models for patterns resembling cognitive biases, but they examine different tasks. Their findings cannot be combined into a single score for “AI bias” or used to rank models across real-world use.
| Study | What was tested | Reported finding | What it does—and does not—show |
|---|---|---|---|
| Macmillan-Scott and Musolesi, Royal Society Open Science (2024) | Seven language models on tasks drawn from cognitive psychology research. | The models gave irrational responses; their errors often differed from human-like bias patterns, and their answers were significantly inconsistent. | Models can fail on psychology-derived tasks in ways that do not simply replicate human biases. It does not establish that every model behaves alike. Study. |
| Koo et al., Findings of ACL 2024 | CoBBLer, an evaluation of six cognitive biases in LLMs acting as evaluators, including preference for their own outputs. | The study reported strong indications of bias in 40% of comparisons and an average Rank-Biased Overlap (RBO) of 44% for human-machine preferences. | These figures describe that study’s comparisons. RBO measures overlap between ranked preferences; 44% is not a general accuracy rate or a universal measure of bias. Study. |
| Malberg et al., NLP4DH 2025 | A framework and dataset covering 30 cognitive biases, with 30,000 tests applied to 20 LLMs in decision-making scenarios. | The paper reports this benchmark and its evaluation of 20 models. | It shows that broad, structured measurement is underway, not that there is one definitive score for every AI system. Study. |
Why the results are not a simple human-versus-AI comparison
The studies measure different things: responses to psychology tasks, rankings made by models acting as evaluators, and decisions tested against a framework spanning multiple biases. They also use different prompts, benchmarks, model sets and scoring methods. A result in one setup cannot automatically be generalized to another task or deployment.
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For example, an evaluator’s preference for its own outputs is a ranking behavior, not evidence that it experiences self-regard. Likewise, an answer that resembles anchoring on a benchmark is an observable pattern; it does not prove the model uses a human mental shortcut. The 2024 psychology-task study’s finding of inconsistency is a further reason to avoid treating one answer as a stable disposition.
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When reading a claim or evaluating a system, look for the details that make the result interpretable:
- Model: Which model and version were tested?
- Task: What question, decision or ranking did it perform?
- Prompt and conditions: What instructions and inputs were used, and were they repeated?
- Comparison baseline: Were outputs compared with human judgments, a benchmark answer or another model?
- Scoring method: What counted as bias, and how was it measured?
- Scope: Does the result apply only to the studied setup, or is there evidence for the intended real-world use?
Without these details, a broad statement that “AI is biased” may blur together a model’s measured output pattern, the data and decisions behind it, and the effects of using the system in an institution.
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What the evidence does not establish
- That AI systems experience thoughts, feelings or biases in the human psychological sense.
- That every AI system has the same bias profile.
- That one current model is the least biased in real-world use.
- That a single prompt eliminates bias-like effects.
- That AI as a whole is more or less biased than people; the cited studies do not provide a common, general comparison.
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