Decision context
AI-assisted decisions
Use these evidence-reviewed lenses when a chatbot, model, recommendation system, or automated decision aid is influencing what you believe or do.
Decision skill
AI-assisted reasoning
Use AI as a decision aid without confusing fluent output, humanlike presentation or repeated generated claims with verified capability and evidence.
Learning outcome: You can use an AI system to expand reasoning while keeping important judgments independently checkable.
When it matters
Notice the situation before naming the bias.
- A chatbot recommendation is influencing an important decision.
- The model gives the first number, score or estimate in the discussion.
- A polished conversational style makes the system feel more capable than you have tested.
- You are using AI mainly to strengthen a conclusion you already prefer.
Practice the skill
Use a better reasoning move.
- Write a provisional judgment or success criterion before asking AI for advice when the decision matters.
- Separate interface quality from the capability needed for the task.
- Verify consequential claims against an independent source rather than another generated answer.
- Ask for counterevidence and alternative explanations before finalizing the decision.
Decision contexts
Apply the skill to real kinds of decisions.
Decision context
Checking claims & misinformation
Use these evidence-reviewed lenses when deciding whether a repeated claim is trustworthy, checking a correction, or testing an explanation against plausible alternatives.
Decision context
Work & project decisions
Use these evidence-reviewed lenses when a team is choosing a direction, interpreting results, deciding whether to continue, or turning metrics into action.
Evidence-reviewed lenses
Patterns worth checking, not labels for people.
Several patterns can fit the same situation. Use these lenses to ask better questions, then inspect the evidence behind each concept.
Automation Bias
Automation bias is a documented pattern of inappropriate reliance on automated cues or recommendations. It can produce commission errors when a user follows incorrect advice and omission errors when a user fails to act because automation did not signal a problem. This does not mean automation is generally harmful: decision support can improve overall performance, and the relevant question is whether reliance remains calibrated when the system is wrong, incomplete, or difficult to verify.
established, context-dependent Read evidence review
Anchoring Effect
Anchoring is a well-supported effect in which an initial numerical value can pull a later estimate toward it. A 2026 meta-analysis covering 2,601 effect sizes found a large overall effect, but also substantial variation across studies. The effect should not be treated as a rule that every number changes every judgment: incidental anchors, anchors from a different dimension, clearly random values, incentives, and some debiasing conditions were associated with smaller or null effects.
well-supported for numerical judgments; strength depends on anchor type and context Read evidence review
Anthropomorphism
Anthropomorphism is the attribution of humanlike properties, intentions, emotions, or mental states to nonhuman agents. It is a well-established psychological phenomenon, but it is not automatically a cognitive error: humanlike models can sometimes be useful. The risk appears when humanlike cues are treated as evidence for capabilities, understanding, accuracy, consciousness, or motives that have not actually been demonstrated.
established attribution tendency; not inherently an error Read evidence review
Appearance–Capability Expectation
Research supports the broader pattern that a robot's appearance, morphology, framing, and human-likeness shape expectations about its competence, social qualities, and likely behavior. However, 'Form-Function Attribution Bias' is not an established standardized name in the literature. On this site it should be treated as a project label for appearance-driven capability expectations, not as a universally recognized cognitive-bias construct.
supported HRI pattern; project label is nonstandard Read evidence review
Confirmation Bias
Confirmation bias is an umbrella label for several ways existing beliefs or hypotheses can influence information search and interpretation. It should not be reduced to one behaviour such as reading only agreeable news, and a preference for confirming tests is not irrational in every task or environment.
well established, broad construct Read evidence review
Illusory Truth Effect
Repeated claims receive higher truth ratings than comparable new claims on average, but the effect is heterogeneous and does not generalize equally to every kind of statement. A 2026 systematic review and meta-analysis covering 182 studies, 366 effect sizes and more than 31,000 participants found a reliable corrected average effect. A later 2026 paper with two preregistered experiments found little or no meaningful repetition effect for social-political opinion statements. The useful claim is therefore narrower than 'repeat anything and people will believe it.'
robust average effect with important content boundary conditions Read evidence review
Use it with an AI agent
Install this decision workflow as an Agent Skill.
The portable version uses the same decision method and keeps bias labels as evidence-aware lenses rather than diagnoses.
Practice this skill
Apply the capability across real decisions.
Before reviewing the source material, a researcher asks an AI assistant for the likely explanation. The rest of the search follows the categories in that first answer.
Skill: AI-assisted reasoningOpen scenario →An AI assistant gives a clear recommendation and names several studies, but the citations cannot be located. The team wants to use the recommendation because it sounds complete.
Skill: AI-assisted reasoningOpen scenario →Five generated summaries repeat the same market statistic. The team treats the repetition as strong agreement, but every answer appears to depend on the same unverified report.
Skill: AI-assisted reasoningOpen scenario →An AI evaluator is asked to independently rescore a report, but the prompt includes the previous model's score and comments. The team treats the second score as independent confirmation.
Skill: AI-assisted reasoningOpen scenario →A review reports several cognitive biases across AI-assisted medical decisions. A team wants to say those biases are established in its own specialist workflow, even though the review contains almost no direct studies from that workflow.
Skill: AI-assisted reasoningOpen scenario →