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Bad AI Doesn't Smell
AI models lack the 'smell' of bad data, leading to dangerous confidence even when outputs are factually incorrect or hallucinated.
What: The article warns that authority bias and truth bias cause users to trust AI outputs blindly, particularly in user research. Because AI cannot signal uncertainty in the same way human data might, researchers must manually verify any AI-generated analysis.
Why it matters: There is a systemic risk in relying on generative AI for qualitative analysis, where subtle nuances like sarcasm or body language are routinely misinterpreted by models despite high-confidence delivery.
Takeaway: Treat AI analysis only as a 'sense-check' tool; never use it as a primary source of truth without human validation.
Original article
Like a bad survey, bad AI output gives no sign that it's wrong, but authority bias, truth bias, and algorithm appreciation make people trust it anyway. In user research, mistranscriptions, missed body language, misread sarcasm, and hallucinations can turn into confident, wrong insights. Someone who ran the research has to check any AI analysis, ideally used only as a sense-check after doing their own.
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