After five lessons celebrating what AI can do, here's the lesson that protects you. By the end, you'll spot AI mistakes that fool most people — and you'll know exactly when not to trust the answer.
See the cracksEach one is a different failure of a different system. Each one is a real risk you've probably already encountered without noticing. Each one is fixable — if you know what to look for.
The AI confidently states things that are completely false. Made-up quotes. Fabricated citations. Invented studies. Often delivered with the same tone as a verified fact.
AI inherits the prejudices baked into its training data — sometimes amplifying them. Decisions that look "objective" hide patterns of historical discrimination.
AI-generated images, voices, and videos so convincing that you can't reliably tell them from real ones. Used in fraud, political manipulation, and harassment.
Tiny changes that fool AI completely. A few pixels added to an image. A subtle audio overlay. The AI confidently misreads it; humans don't even notice the change.
Each one sounds plausible. Each one could go either way. Trust your gut — but check it at the end. Most people score 4–5 out of 8. The world's worst score: most students who started this module.
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AI doesn't invent its bias. It inherits the patterns we already had — and then it scales them up to millions of decisions. Watch what happens.
100 fictional applicants — 50 women (pink), 50 men (blue). Equal qualifications. Watch how a model trained on 10 years of past hiring decides.
Used in US courts to predict re-offending risk. ProPublica showed it falsely flagged Black defendants as "high risk" at nearly 2x the rate of white defendants.
Joy Buolamwini found leading face-recognition systems were 99% accurate on white men — and as low as 65% on Black women. Same model, different reliability.
A widely-used US healthcare algorithm directed less care to Black patients. It used past healthcare spending as a proxy for "sickness" — but Black patients historically receive less care, so the proxy hid the bias.
Studies showed asking image AIs for "a CEO" almost always returned a white man; "a janitor" returned a person of color. The stereotypes weren't programmed — they were absorbed from billions of internet images.
Generative AI is getting better fast. These tells used to be obvious in 2022. In 2026, they're more subtle — but most still hold. The faster you can spot them, the safer your judgment.
Six fingers. Three fingers. Fingers melting together. Hands are still the #1 tell — AIs were trained on photos where hands are usually relaxed and partially hidden.
Words on signs, t-shirts, books — AI almost always botches them. Letters that aren't quite letters. Words that look right at a glance and gibberish on close inspection.
Earrings that don't match. Eyes slightly different sizes. Eyebrows shaped differently. Two glasses lenses that warp differently. The AI generates each side independently — and they rarely agree.
Mirrors that don't reflect what's in front of them. Shadows falling the wrong direction. Light sources that physics wouldn't allow. AI doesn't simulate physics — it pattern-matches what photos look like.
Foreground subjects look sharp. But edges in the background often warp, melt, or smear. Lines that should be straight curve subtly. Patterns that should repeat shift mid-image.
Top-tier AI images can already pass casual inspection. The tells are getting subtler. Verifying source matters more than spotting AI. If a photo claims something important (a politician said X, a celebrity did Y) — find the original source, the news organization, the verified account. If you can't, treat it as fiction.
Print these. Tape them to your monitor. The people who get the most out of AI follow them obsessively.
Every AI response is a starting point. Read it critically. Fact-check claims that matter. The mistake isn't using AI — it's accepting its output without review.
Names, dates, statistics, citations, legal facts, medical facts — anything specific must be confirmed against a real source. The AI is wrong with confidence; you need to be right with verification.
Medical diagnoses, legal advice, financial moves, hiring decisions — the AI may help you think, but humans (and licensed experts) must decide. Liability and judgment don't transfer to a machine.
Hallucinations sound great. They mimic the texture of truth — confident, specific, well-structured. That smoothness is a warning sign, not a confirmation. Real research has rough edges.
Don't ask "is this AI?" Ask "where did this come from?" A verified source from a known publication beats any AI-detection tool. Trace the origin — most fakes don't have one.
Aim for 4/5. Wrong answers explain themselves.
You know the four failure modes. You played the Real-or-AI game. You can spot deepfake tells. You have 5 rules taped to your forehead. That's a real, practical, professional-grade skill — and you'll use it tomorrow.
Continue to Module 07