LLMs Amplify our Cognitive Biases
Because LLMs responses are fluent, confident, personalized and well structured, they can make an idea feel more accurate or well supported than it really is.
The risk is not simply that the AI may be wrong, but this it interacts with our human biases making us less likely to notice weak assumptions, missing information or alternative explainations.
Confrimation Bias
We favour information that supports what we already believe.
When a question contains an assumption, an LLM can build a remarkably persuasive case. The danger is a well-written answer can feel like independent validation when the model may simply be reasoning within the frame we gave it
Authority Bias
We give more weight to information when it appears to come from an authoritative source.
LLMs communicate with a tone, confidence, structure and specificity that we associate with experts. They may incorporate our company, industry, and data making the advice feel like it comes an expert who deeply understands our business.
But authoritative presentation is not the same as authoritative knowledge.
Forer Effect
Generic observations feel highly personal when we recognize ourselves in them.
LLMs can take broadly applicable advice (“focus on your strongest customers”) and surround it with details to feel highly customized. Personalization can make generic advice feel like unique insight.
WYSIATI
What You See Is All There Is
We naturally create coherent stories from the information available and often fail to notice what is missing. LLMs amplify this because they produce complete, polished narratives with few visible gaps.
It is more important to ask: what information is missing? What assumptions were made? What alternatives were never considered?